There are many good ideas and practices in Agile development, ideas and practices that definitely work: breaking projects into Small Releases to manage risk and accelerate feedback; time-boxing to limit WIP and keep everyone focused; relying only on working software as the measure of progress; simple estimating and using velocity to forecast team performance; working closely and constantly with the customer; and Continuous Integration – and Continuous Delivery – to ensure that code is always working and stable. But there are other commonly accepted ideas and best practices that aren’t important: if you don’t follow them, nothing bad will happen to you and your project will still succeed. And there are a couple that you are better off not following at all. Test-Driven Development Teams that need to move quickly need to depend on a fast, efficient testing safety net. With Test First Development or Test-Driven Development (TDD), there’s no excuse for not writing tests – after all, you have to write a failing test before you write the code. So you end up with a good set of working automated tests that ensure a high level of coverage and regression protection. TDD is not only a way of ensuring that developers test their code. It is also advocated as a design technique that leads to better quality code and a simpler, cleaner design. A study of teams at Microsoft and IBM (Realizing Quality Improvement through Test Driven Development, Microsoft Research, 2008) found that while TDD increased upfront development costs between 15-35% (TDD demands developers change the way that they think and work, which slows developers down, at least at first), it reduced defect density by 40% (IBM) or as much as 60-90% (Microsoft) over teams that did not follow disciplined unit testing. But in Making Software Chapter 12 “How Effective is Test-Driven Development” researchers led by Burak Turhan found that while TDD improves external quality (measured by one or more of test cases passed, number of defects, defect density, defects per test, effort required to fix defects, change density, % of preventative changes) and can improve the quality of the tests (fewer mistakes in the tests, tests that are easier to maintain), TDD does not consistently improve the quality of the design. TDD seems to reduce code complexity and improve reuse, however it also negatively impacts coupling and cohesion. And while method and class-level complexity is better in code developed using TDD, project/package level complexity is worse. People who like TDD like it a lot, so if you like it, do it. And even if you are not TDD-infected, there are times when working test first is natural – when you have to solve a specific problem in a specific way, or if you’re fixing a bug where the failing test case is already written up for you. But the important thing is that you write a good set of tests and keep them up to date and run them frequently – it doesn't matter if you write them before, or after, you write the code. Pair Programming According to the VersionOne State of Agile Development Survey 2012, almost 1/3 of teams follow pair programming – a surprisingly high number, given how disciplined pair programming is, and how few teams follow XP (2%) or Scrum/XP Hybrid (11%) methods where pair programming would be prescribed. There are good reasons for pairing: information sharing and improving code quality through continuous, informal code reviews as developers work together. And there are natural times to pair developers, or sometimes developers and testers, together: when you’re working through a hard design problem; or on code that you’ve never seen before and somebody who has worked on it is available to help; or when you’re over your head in troubleshooting a high-pressure problem; or testing a difficult part of the system; or when a new person joins the team and needs to learn about the code and coding practices. Some (extroverted) people enjoy pairing up, the energy it creates and the opportunities it provides to get to know others on the team. But forcing people who prefer working on their own or who don’t like each other to work closely together is definitely not a good idea. There are real social costs in pairing: you have to be careful to pair people up by skill, experience, style, personality type and work ethic. And sustained pair programming can be exhausting, especially over the long term – one study (Vanhanen and Lassenius 2007) found that people only pair between 1.5 and 4 hours a day on average, because it’s too intense to do all day long. In Pair Programming Considered Harmful? Jon Evans says that pairing can have also negative effects on creativity: Research strongly suggests that people are more creative when they enjoy privacy and freedom from interruption … What distinguished programmers at the top-performing companies wasn’t greater experience or better pay. It was how much privacy, personal workspace and freedom from interruption they enjoyed,” says a New York Times article castigating “the new groupthink”. And in “Still Questioning Extreme Programming” Pete McBreen points out some other disadvantages and weaknesses of pair programming: Exploration of ideas is not encouraged, pairing makes a developer focus on writing the code, so unless there is time in the day for solo exploration the team gets a very superficial level of understanding of the code. Developers can come to rely too much on the unit tests, assuming that if the tests pass then the code is OK. (This follows on from the lack of exploration.) Corner cases and edge cases are not investigated in detail, especially if they are hard to write tests for. Code that requires detail thinking about the design is hard to do when pairing unless one partner completely dominates the session. With the usual tradeoff between partners, it is hard to build technically complex designs unless they have been already been worked out in a solo session. Personal styles matter when pairing, and not all pairings are as productive as others. Pairs with different typing skills and proficiencies often result in the better typist doing all of the coding with the other partner being purely passive. And of course pairing in distributed teams doesn't work well if at all (depending on distance, differences in time zones, culture, working styles, language), although some people still try. While pairing does improve code quality over solo programming, you can get the same improvements in code quality, and at least some of the information sharing advantages, through code reviews, at less cost. Code reviews – especially lightweight, offline reviews – are easier to schedule, less expensive and less intrusive than pairing. And as Jason Cohen points out even if developers are pair programming, you may still need to do code reviews, because pair programming is really about joint problem solving, and doesn’t cover all of the issues that a code review would. Back to Jon Evans for the final word on pair programming: The true answer is that there is no one answer; that what works best is a dynamic combination of solitary, pair, and group work, depending on the context, using your best judgement. Paired programming definitely has its place. (Betteridge’s Law strikes again!) In some cases that place may even be “much of most days.” But insisting on 100 percent pairing is mindless dogma, and like all mindless dogma, ultimately counterproductive. Emergent Design and Metaphor Incremental development works, and trying to keep design simple makes good sense, but attempting to define an architecture on the fly is foolish and impractical. There’s a reason that almost nobody actually follows Emergent Design: it doesn't work. Relying on a high-level metaphor (the system is an "assembly line" or a "bill of materials" or a "hive of bees") shared by the team as some kind of substitute for architecture is even more ridiculous. Research from Carnegie Mellon University found that … natural language metaphors are relatively useless for either fostering communication among technical and non-technical project members or in developing architecture. Almost no one understands what a system metaphor is any ways, or how it is to be used, or how to choose a meaningful metaphor or how to change it if you got it wrong (and how you would know if you got it wrong), including one of the people who helped come up with the idea: Okay I might as well say it publicly - I still haven't got the hang of this metaphor thing. I saw it work, and work well on the C3 project, but it doesn't mean I have any idea how to do it, let alone how to explain how to do it. Martin Fowler, Is Design Dead? Agile development methods have improved development success and shown better ways to approach many different software development problems – but not architecture and design. Daily Standups When you have a new team and everyone needs to get to know each other and more time to understand what the project is about; or when the team is working under emergency conditions trying to fix something or finish something under extreme pressure, then getting everyone together in regular meetings, maybe even more than once a day, is necessary and valuable. But whether everyone stands up or sits down and what they end up talking about in a meeting should be up to you. If your team has been working well together for a while and everyone knows each other and knows what they are working on, and if developers update cards on a task board or a Kanban board or the status in an electronic system as they get things done, and if they are grown up enough to ask for help when they need it, then you don’t need to make them all stand up in a room every morning. Collective Code Ownership Letting everyone work on all of the code isn't always practical (because not everyone on the team has the requisite knowledge or experience to work on every problem) and collective code ownership can have negative effects on code quality. Share code where it makes sense to do so, but realize that not everybody can – or should – work on every part of the system. Writing All Requirements as Stories The idea that every requirement specification can be written as User Stories in 1 or 2 lines on cards, that requirements should be too short on purpose (so that the developer has to talk to someone to explain what’s really needed) and insisting that they should all be in the same template form “As a type of user I want some goal so that some reason…” is silly and unnecessary. This is the same kind of simple minded orthodoxy that led everyone to try to capture all requirements in UML Use Case format with stick men and bubbles 15 years ago. There are many different ways to effectively express requirements. Sometimes requirements need to be specified in detail (when you have to meet regulatory compliance or comply with a standard or integrate with an existing system or implement a specific algorithm or…). Sometimes it’s better to work from a test case or a detailed use case scenario or a wire frame or some other kind of model, because somebody who knows what’s going on has already worked out the details for you. So pick the format and level of detail that works best and get to work. Relying on a Product Owner Relying on one person as the Product Owner, as the single solitary voice of the customer and the “one throat to choke” when the project fails, doesn't scale, doesn't last, and puts the team and the project and eventually the business at risk. It’s a naïve, dangerous approach to designing a product and to managing a development project, and it causes more problems than it solves. Many teams have realized this and are trying to work around the Product Owner idea because they have to. To succeed, a team needs real and sustained customer engagement at multiple levels, and they should take responsibility themselves for making sure that they get what they need, rather than relying on one person to do it all.

A couple of weeks ago we looked at how to do a quick "health check" of an agile team. We saw that a great deal can be learned just by attending one of their daily stand-ups and by inspecting the state of their Scrum and Kanban boards. Of course these are nothing more than cursory examinations, and serious ailments can lie behind an apparently robust façade of agile practice. If you have reason to believe that a team is dysfunctional, you might have to dig deeper than the superficial evidence suggested by its apparent morphology. In my experience the next thing to examine is the team's "Definition of Done". This is the standard to which all work is put before it can be considered to be complete. Each team is collectively responsible for its own Definition of Done. It's up to them to make sure that it is adequate, and that it is applied by all members to all of the work they do. Without such professional oversight there can be no assurance that any deliverable will truly be fit for release. A spiffy stand-up and a cracker of a Kanban board might suggest rude team health, but they are no guarantee that the Definition of Done is solid, or that it isn't being undercut by someone along the way. Technical debt and rework are the main symptoms to look for. The consequences of backsliding on a Definition of Done might not become apparent until long after the events that caused it. By then, that rework or debt can be difficult to trace to the specific behaviors of those who cheated the system. You see, unfortunately a Definition of Done is a bit like personal hygiene. If there is no oversight, everyone can pretend that they are following the rules for the sake of the team, even though the presence of E. Coli on the office keyboards will tell its own tale about compliance. Everyone knows that it has to be coming from somewhere, but won't admit to their own liability or involvement, perhaps not even to themselves. Just as team vomiting will follow one member's dubious hand-washing practices, a short-changed Definition of Done will lead to rework by the team or the creation of technical debt. This is why team ownership and enforcement of what "Done" means is so important. An effective Definition of Done has to be founded on a healthy balance between due diligence and professional trust. What does this mean for agile development? You can think of a Definition of Done as the key defensive bulwark in software development epidemiology. If you balance the right level of team oversight with the right level of trust, severe outbreaks of technical debt or rework will become rare. High levels of oversight may be needed to start with, since team members might not have bought in to the idea of "done" yet. Once people become conditioned to do the right thing and see themselves as stakeholders in the team and its success, the balance can swing more towards trust. People become reluctant to renege on a team investment if they can see that it adds value for everyone including them. What's more, a Definition of Done improves the more it is respected, and becomes better respected the more it improves. In terms of agile best practice a Definition of Done will be used to determine whether or not User Story implementations are release-ready. However, each team can implement many User Stories over the course of a sprint, and making sure that all of these stories meet the Definition of Done can be challenging. Teams that are new to agile methods often have more modest ambitions. For example, their Definition of Done may only extend as far as delivery into a pre-production environment. Of course, anything less than "fully release ready" incurs the risk of technical debt and the need to pay it back later. Yet like a sloppy approach to hand-washing, it has to be admitted that something is better than nothing at all. Applying a Definition of Done consistently to even a sub-optimal standard will at least permit the delivery of each User Story to a known level of quality. It might not be great but at least it's there, and it's something that can be built upon and improved. The Lessons of Lean-Kanban Lean-Kanban methodologies have an instructive relationship with the Definition of Done. In these approaches the optimization of the value stream is of great significance. Naturally though, if a value stream is to be optimized it must first be understood. This means breaking the stream down into multiple discrete steps that can be studied for bottlenecks and any other occurrences of waste. For example, "Work in Progress" can be broken down into finer-grained stations such as "In Development", "Peer Review", "QA Test", "Knowledge Transfer", and "In Deployment". Team members will be cross-trained and will move freely across those stations in order to expedite as smooth a workflow as possible. Now here's where it gets interesting. If a Lean-Kanban operation has multiple well-defined stations, the case for having a Definition of Done can seem rather harder to make. After all, by the time a User Story gets to "Done", you already know that it has gone through the key steps you care about in the development process. What value can a Definition of Done really add in such a situation? Doesn't it just become waste itself? To find the answer, we need to look back to the manufacturing roots of Lean-Kanban. In a car plant for example, the steps of construction are exceptionally well defined and team members can move freely over several dozen stations. Some of those stations will be for the chassis, others for the interior, others for the engine block and electrics and so on. Yet despite this the Definition of Done will be an absolute corker, and much of the process of verification will be automated. Each station might even have its own Definition of Done so inspection can occur as close as possible to the point of action and potential remedy. The total number of checks that happen before each vehicle leaves the factory will be exhaustive. Why is this thought to be necessary? Because the manufacturers know perfectly well that the verification of "done" adds value. Merely having well-defined stations is no guarantee that everything will be done well. Quality is built in and validated by inspection. One thing's for sure: no-one in IT should accuse car manufacturers of having a weak understanding of what "done" means. The Definition of Done versus Acceptance Criteria However, software projects have a wild-card to deal with that car manufacturers don't have to worry about. Unlike the car doors and carburettors that roll down an assembly line, each User Story is different and has to be treated as a special case. To deal with this, each User Story has Acceptance Criteria that are agreed by the team members and the Product Owner as part of a Sprint Planning Session. Acceptance Criteria have to be quite specific to particular User Stories, because each story can be unique. The Definition of Done, on the other hand, applies to all of the User Stories being worked on by a team. The associated conditions must be invariant. For example, if all work has to be peer reviewed and subjected to QA testing prior to release, then those criteria would be enumerated in the Definition of Done rather than being repeated in each User Story's Acceptance Criteria. If the definition is enforced properly, a developer could never claim that a User Story was “Done” if it hadn't both been reviewed and passed QA testing. Writing a Definition of Done The Scrum Guide describes a Definition of Done as a "shared understanding of what it means for work to be complete". No process is suggested for writing a Definition of Done, nor in fact is there any suggestion that one should be written down at all. However, a documented definition may go some way towards providing that shared understanding. Here's how you can set about eliciting one: Review Acceptance Criteria: Gather the Acceptance Criteria for work completed so far Look for common criteria that can be abstracted out and applied across work in general Use these common criteria as the basis for a Definition of Done Assess Technical Debt Identify any rework that needs to be done Identify the reasons why it wasn't done properly the first time Identify what measures can be put in place to stop similar rework from occurring Add these measures to the Definition of Done (DoD) Continually update the DoD In each Sprint Review, identify which (if any) work was rejected or which caused rework to be done, then In each Sprint Retrospective, challenge the DoD for relevance and completeness There isn't a prescribed format for a Definition of Done, but it can be beneficial to use the same as that which is used for Acceptance Criteria, either in whole or in part. This allows a flexible definition based on story type. Alternatively they can be written as simple lists. Here are some examples: Example of a Definition of Done in Acceptance Criteria Format Given that a user story has required a code change When BDD and unit tests have been written for the story and the code change has been reviewed and the code change has been approved by a peer and all BDD and unit tests have been run and no BDD or unit tests have broken (green bar) and the code change has been committed to the repository and QA testing has passed satisfactorily and the Product Owner has approved the change Then the user story will be deployed to production and it will be considered Done. Given that a user story has required the authoring of documentation When the documentation has been reviewed and approved by a peer and the documentation has been approved by the Product Owner Then a new version of the documentation will be committed and the user story will be considered Done. Example of a Definition of Done in List Format Code changes: BDD tests written and pass Unit tests written and pass Code peer reviewed & approved Code committed to repository QA testing done Product Owner signed off Documentation: Documentation has been peer reviewed & approved Documentation approved by Product Owner Version number updated Definitions of Done for IT Infrastructure Support We've seen that having a good Definition of Done is important, although in IT we also need Acceptance Criteria that address the particulars of each User Story. When used in combination they can approach the levels of rigor that have been shown to be possible in manufacturing. Those working in software development can adopt a similar commitment to quality. Now we need to turn our attention to another function within the IT department...Infrastructure Support. Infrastructure support teams are increasingly expected to work in an agile way. As part of an enterprise transformation that does not seem unreasonable. After all, the rest of the organization is highly dependent upon them. Their scope includes such things as deploying new workstations and laptops (possibly across entire sites), installing networks, performing miscellaneous diagnostics and repairs, and maintaining and upgrading local server resources. Clearly they will also need Definitions of Done and Acceptance Criteria if they are to be stakeholders in a joined-up agile enterprise. The question is, how on earth can a meaningful Definition of Done be abstracted across wildly different physical tasks? How can a Definition of Done cover everything from a phone installation to a printer driver upgrade or a memory enhancement, or a firewall configuration to a keyboard replacement? The answer is to focus on the value chain that is represented by each user story. Work is not "released" as such, but rather it is handed over to someone who can derive benefit from it (i.e. the person occupying the user story role). This is the key to understanding "done" in an infrastructure context. If you can identify the parties who derive value, and demonstrably pass that value on to them, then your work is done. Here's an example of a Definition of Done that might be used to close out a support ticket: The receiver of the work has been identified Handover instructions have been completed and given to the receiver The receiver has been notified of the intention to close the ticket, and has not raised an objection A security assessment has been conducted and approved There are three things to point out here. The absence of any reference to a Product Owner. This is because infrastructure teams have to support multiple products, and prioritization of work is traditionally handled not through any sense of ownership of those products, but rather through help-desk triage. It's certainly possible for work to be represented by Product Owners, but it would have to be ownership of the business support function rather than ownership of the actual products being supported. The need to identify and work with the actual receivers of value is still there. The "acceptance by default" position. Receivers typically have little incentive to sign work off as being complete. On the contrary, their incentive is to defer acceptance as long as possible, for potential use as a "banker" in case a requirement for additional unforeseen work transpires. They might hope to ride this new work on an existing ticket instead of having to raise a new one. Receivers can be expected to care about their own support needs, not about the big picture of enterprise delivery. If a Product Owner can be identified - even if it is just the most likely owner of the business support function - then this situation can be improved. A Product Owner can apply leverage for appropriate and timely sign-off, such as by not accepting new work from certain parties while their approval (or justified rejection) of prior work remains outstanding. The elicitation of solid Acceptance Criteria can help the Product Owner immensely. Security implications need to be given careful consideration. The reworking of organizational infrastructure offers great potential for security to be compromised. Approval from Information Security should be obtained for all work, either directly or through authorized agents. One approach is for each team to have a designated "security champion" who provides this function. Conclusion Teams that appear to be healthy can still incur rework and technical debt. A poor understanding of what "done" means often underlies such problems, and this should be one of the first things to be looked at if problems are suspected. Having a meaningful Definition of Done encourages a team's sense of ownership of their own process, and helps instil self-discipline into its members to follow agile best practices. The application of this standard requires finding the right balance between team oversight and trust.

The longer I spend working as an agile coach, the more I find myself in disagreement with Hamlet. To estimate, or not to estimate? That is the question. Out of all of the agile practices which have been adopted in recent years, few have proven more controversial than this one. The battle for and against rages like Shakespearean armies set against each other's teeth. (Free Estimation Ebook) At first blush there doesn't seem to be any reasonable cause for disagreement. The rationale for making estimates is ostensibly straightforward. If a team is to work in Sprints, and to deliver something at the end of each one, then the work must surely be estimated. Otherwise how can the team know if it is even possible to do the work within the Sprint? How can they commit to deliver something by the end of that time-box if the effort involved is of uncertain magnitude? Well, there are two things that we need to draw out at this point. Firstly, the above rationale assumes that Sprints will be used, and that delivery will therefore be time-boxed. That's a very Scrum oriented philosophy...but Scrum isn't the only agile way of working. Lean-Kanban teams, for example, don't use Sprints and rarely make use of estimates. Secondly, Scrum itself says nothing about estimation. It only says that each item in a backlog must be sized - how that sizing happens is up to the team. It should also be remembered that a Scrum team commits to a Sprint Goal that delivers value, not to the delivery of a certain number of estimated points. So then...to estimate, or not to estimate? Let's listen in at the camp-fires of each side, and pick out in more detail the arguments they make for and against. For (Ye Scrum Brigade of Sprinte and Stande-uppe) "Estimates allow us to predict when a Sprint Goal will be met, and therefore when a substantial increment of value will be delivered" "Our estimates help our stakeholders plan ahead. They are part of the value we provide" "Estimates help us to de-risk scope of uncertain size and complexity" "Estimated work can be traded in and out of scope for other work of similar size. Without estimates you can't trade" "The very process of estimation adds value. When we estimate we discuss requirements in more detail, and gain a better understanding of what is needed" Against (Ye Lean Kanban Brigade of Boarde Pullers) "Estimates are rarely accurate. All you are doing when you estimate a piece of work is to set false expectations" "In practice, estimation is seen as a commitment, not as a best guess. Every time you make an estimate, you make a rod for your own back" "Estimation is time consuming. The time a team spends playing planning poker or whatever is time that could have been spent on delivery. Estimation is waste." "It's the actuals that matter, not estimates. Agility requires metrics, and the only metrics that count are those that reflect actual delivery" Both sides are right If you see this debate in terms of whether a Scrum or Lean-Kanban process is being followed, then both sides are right. A Scrum process is optimized for project work where scope risk is high and an entire system is represented. The requirements tend to be uncertain, complex, and very heavily intertwined. By committing to a Sprint Goal and to the delivery of a substantial increment of value, that risk can be managed. Uncertain and interdependent requirements are batched together into a Sprint and dealt with as a group. When this is done well, you have a clear Sprint Goal and a coherent Sprint Backlog. When it is done badly, you have a vague or disjointed Sprint Goal, a mishmash of requirements that command no sense of team purpose, and no team commitment towards the delivery of an increment. A Lean-Kanban process, on the other hand, is usually focused on "Business As Usual" (BAU) activities. The diet of a Lean-Kanban operation should consist of small and repeatable changes. They don't have to be related at all...in fact they shouldn't be. Things like bug fixes, minor enhancements, and administrative tasks are representative of this kind of work. Scope risk is low because the process of making such changes is well understood. Estimates are generally held to be unnecessary because there is very little uncertainty to deal with. There is no need for work to be batched...each change can be actioned and delivered independently of all others. Work is enqueued and actioned according to priority and the required quality of service. Predictions are based on the actual rate of delivery, not on estimates. In a Lean-Kanban way of working, the actuals are indeed everything. Methods of estimation So then, estimates add value where scope is uncertain and there are associated risks to be managed. That's why Scrum teams engaged on projects typically make use of them, but Lean-Kanban BAU teams generally don't. Now let's look at three simple methods of estimation that Scrum teams, or other teams doing project work, can make use of. Planning Poker This is a well established technique popularised by Mike Cohn, and variations on his Planning Poker cards can be found in offices across the world. A typical Planning Poker set has cards with the following numbers: ½ 1 2 3 5 8 13 20 40 100. Nerds will observe and be irritated by the fact that this is roughly (but not quite) in line with the Fibonacci sequence. Here's how it's played: An identical hand of cards is given to each team member. Each team member will have a set of cards with numbers on the above pseudo-Fibonacci scale. The Product Owner describes the piece of work to be estimated. Normally this is a user story with acceptance criteria. Each team member mentally estimates the size of it on the scale. They can ask the Product Owner questions to clarify any points, but for the moment they will keep their estimate to themselves. Each team member places the card that corresponds to their mental estimate face down in front of them. At the facilitators instruction - usually the Scrum Master - the team turn their cards over. In an ideal case the cards will all have the same value, suggesting that the team have a common understanding of the requirements and the likely effort that will be involved. If the values are different, the team then need to discuss their estimates and their reasoning behind them. They need to understand each other’s thinking, and from that reach a consensus. It may be necessary to replay the cards several times before agreement is reached. By convention, estimates are written on the corner of a User Story card before being placed on a Scrum board. A variation of this takes from a suite of regular playing cards. The Ace (1), 2, 3, 5, 8, Jack, Queen, and King might be used. The Jack signifies that no or negligible work needs doing (jack all). The Queen indicates a larger story that should be broken down in the planning session and reconsidered, while the King indicates an epic that will need greater analysis and cannot be brought into scope for this Sprint. The Joker can be played if anyone wants a coffee break. As an estimation method, Planning Poker has the advantage of being fairly democratic. Every team member gets a hand of cards and is allowed to play, and has a clear opportunity to explain their reasoning to the others. The disadvantage of Planning Poker is that it can be rather time consuming in comparison with other methods. It can also encourage novice teams to estimate in terms of time, as they are often initially prejudiced to correlate points to hours or days. This prejudice must be challenged and eroded if the relative sizing of estimates is to be achieved. Team Sort (T-Shirt Sizing) This is a good way of doing team estimates if no planning poker cards are available. All you need are six scraps of paper and a set of index cards with the requirements (e.g. user stories) written on them. Normally these will be the same index cards that go on the Scrum board. Write one of the following sizes on each of the scraps of paper: Extra Small (XS), Small (S), Medium (M), Large (L), Extra Large (XL), and Extra Extra Large (XXL) Arrange the sizes in a horizontal line on a table, ordered from XS on the left to XXL on the right. Put the pile of index cards on the table in front of the sizing line. The team then collaborate to organise the requirements on the cards under the headings XS to XXL. They can ask the Product Owner to clarify any questions that they may have while doing so. Once the cards have all been sorted, story points can be allocated to each of them by mapping each T-Shirt size to a value. This allows metrics to be gathered about the flow of work, and used to populate a velocity or burndown chart. T Shirt Size Suggested Story Point Value XS 1 S 2 M 3 L 5 XL 8 XXL 13 An advantage of the team sort is that it is quick and easy to do. The complete set of requirements is estimated in one sweep. Also, it is a fairly direct way of achieving relative sizing. There is no temptation to correlate points to hours. The disadvantage is that it is potentially undemocratic, in that assertive team members can dominate meeker ones with their opinions. There is a variant of the team sort which encourages more egalitarian behavior. Each team member takes it in turns to move one card by one position. They also have the option to pass, i.e. to not move a card. Eventually a consensus should be reached and no more cards will be moved. However this is a more time consuming method and deadlocks can occur. These deadlocks can be difficult to spot if multiple card shifts are caught in the cycle. One Point One Card This method is a spin on the Lean-Kanban approach of tracking actuals. Instead of estimating the relative effort required for each story card, the team estimates how many stories it is likely to complete in the Sprint being planned. This can be as straightforward as using the yesterday's weather analogy for velocity estimation. Just as the weather today is most likely to resemble the weather yesterday, the velocity that will be achieved by a team in the upcoming Sprint will most likely match the velocity of its predecessor. So if two dozen cards were completed in the last sprint, approximately two dozen can be expected in the one that follows. The budget can be adjusted to allow for holiday, foreseeable absences, and other such changes that will impact the team's commitment. The advantage of this system is its raw simplicity. The estimation overhead is almost negligible. Also, it encourages the authoring of small user stories that will spend little time in progress and that stand little chance of being impeded. The liquidity of the board is therefore increased and further requirements analysis is encouraged. Some variation in size will be inevitable, and there will be statistical outliers, but the effects of these will average out as the flow rate increases. The disadvantage of this technique lies in the separation of fine-grained user stories from business value. There is a significant risk that they will become excessively technically focused and task-like. Conclusion Agile estimation is often seen as being invaluable, yet others dismiss it as waste. The reasons for this disagreement can be traced to disparities in Scrum and Lean-Kanban ways of working, and to the fundamental differences between project work and Business As Usual. When seen in the context of Scrum projects, some form of estimation process is valuable. Yet regardless of the method chosen, it must be acknowledged that a Scrum Team is responsible for its own estimates. No-one else can make a team's estimates for them. Going through that process of estimation, and understanding the size and scope of the work, is fundamental to the team's sense of Sprint Backlog ownership and to their commitment to a Sprint Goal.

Richard Hundhausen has put together a great list of funny Scrum/Agile related videos. Some of these are classics such as High Moon Studios: Portrait of Scrum and Adam Weisbart’s Shit Bad Scrum Masters say. Be warned, not all of these are actually that funny. I’ve never found The Downfall of Agile Hitler to be funny, because the original film is harrowing and difficult to watch. I also find the Scrum Haka to be trite and unwatchable … this is what a real haka should look like. So here’s the list and, once you’re done (in the words of Adam Weisbart), “Get back to work!” I want to run an agile project http://www.youtube.com/watch?v=4u5N00ApR_k I want to run an agile project (part 2) http://www.youtube.com/watch?v=lAf3q13uUpE The Power of Scrum (Ian Sense Scrum Master) http://www.youtube.com/watch?v=P6v-I9VvTq4The making-of http://www.youtube.com/watch?v=ncjdtqf1gSg Developer Abuse http://www.youtube.com/watch?v=LYlhCGng5Mk Spooning and pair programming http://www.youtube.com/watch?v=dYBjVTMUQY0 Improving Sprint Reviews (is that Jeroen?) http://www.youtube.com/watch?v=fpBQ5yxrR_c The Downfall of Agile Hitler http://www.youtube.com/watch?v=l1wKO3rID9g High moon studios: Portrait of Scrum http://www.youtube.com/watch?v=UT4giM9mxHk Shit Bad Scrum Masters Say http://www.youtube.com/watch?v=GGbsgs611MM The Scrum Haka (hideous) http://www.youtube.com/watch?v=Qvqq97unS2w Joe Justice Team WikiSpeed http://www.youtube.com/watch?v=x8jdx-lf2Dw Deathstar Project Deployment Meeting http://www.youtube.com/watch?v=2T5QNcb_Z8g Raking Leaves – A Scrum/Agile Approach http://www.youtube.com/watch?v=StBS-loIIz4 I Need Agile Methodology http://www.youtube.com/watch?v=nvks70PD0Rs

Here are 10 articles from 10 different authors that provide valuable advice for Scrum teams. These articles are in no particular order, so feel free to skim down the list and start with the ones that are most relevant to you. 10 Tips for a Great Daily Scrum Meeting by Platinum Edge – The daily Scrum meeting is a powerful tool that keeps your project moving. At the same time, it is also easy for the meetings to not bring any added value. Tips for Effective Backlog Grooming by Charles Bradley – Are you wasting time in your Sprint Planning Meetings? Increase the value of your team’s Sprint Planning Meetings by grooming your Product Backlog. Yoda’s top 10 tips for a new Scrum Master by Nigel Steane – As a new Scrum Master, you face unfamiliar challenges and your success is very much based on your ability to utilise coaching and soft skills to gently guide your team and colleagues. Top ten tips for distributed Scrum team teleconferences By Jon Archer – After acting as a Scrum Master for several months on a distributed team with people in six different locations, three different countries, learn ten tips to help get past those inevitable awkward silences. 10 tips for adopting Scrum to save your project by Matthew Hodgson – Are you interested in adopting Scrum for your next project? Here are 10 tips from his experience with moving a number of projects from their existing project management frameworks to Scrum. Five Tips for Impediment Resolution with Scrum by Stefan Roock – Impediments can slow down or even halt the progress of an otherwise well-functioning Scrum team. Take a look at the most common challenges that crop up on teams and what steps you can take to resolve them. 10 Tips for Succeeding with Enterprise Agile Development by Tools Journal – Many enterprises are experimenting with agile development approaches like Scrum, Kanban, Lean, and XP hoping that introducing a new development approach will help. Yet, agile development has struggled to achieve critical mass in large enterprises. 6 Tips for Good Scrum by Martin Harris – If you are doing these 6 tips, then you are doing very well and are likely to get better over time. 9 Tips for Creating a Good Sprint Backlog by Luciano Felix – Giving attention to the sprint backlog creation process is fundamental to the team’s understanding of what should be done and how to better plan during the sprint. 7 Tips for a More Effective Daily Scrum by Richard Lawrence – The main purpose of the Daily Scrum is for team members to make and follow-up on commitments to one another that work towards the team’s shared sprint commitment. Here are seven ways to get your Daily Scrum back on focus If your it has become unfocused, too long, or otherwise ineffective. If you have any other good articles related to agile, please share them in the comments. Thanks.

I've been working as a Scrum Master and as an Agile Coach for a good few years now, mainly as a contractor. Each time I am interviewed for a new contract I always like to ask if I can meet the teams I’d be working with. You see, time and again it will be a manager who does the interviewing, while the team members themselves are left with little say in whether I should be hired. I think it’s important that they reckon they can get along with me. Of course, it also gives me an opportunity to see them, and to gain a fuller understanding of the situation I’d really be walking into. As we head towards the desks of my prospective team, one of the first things I look for is the board, whether it be a Scrum task board or a Kanban board. Most teams with agile aspirations…or agile pretensions…will have set up a board of some kind. A board is the "grand old dame" of information radiators. No matter how much the details of a sordid past are glossed over, the truth always seems to come out. It's in the nature of a board to tell the truth, since any untruths can be quickly exposed. The story I can piece together from dubious lanes and columns, misplaced or missing tickets, misplaced or missing avatars, and a host of other shibboleths can be far more telling than anything I get to hear from people in an interview situation. Another of the things I look for is a "fast track" lane on a Scrum Team's board. These are very common; you could say it is almost unusual not to see them. From a certain perspective they are good things to have, and they can imply a level of maturity - or at least of pragmatism - on the part of a team. They suggest that the team accepts that not everything can be predicted in Sprint planning. A fast track lane is a nod to the fact that emergencies happen, that support work and unforeseen defect fixes still need to be done, and most importantly, that the team has a way of dealing with all of this. However it also shows that they aren't doing Scrum. There...I've said it. Fast track lanes aren't part of Scrum. It's that simple. I don't mean to say that they are bad practice, or in some sense un-agile. On the contrary, they are part of the Lean Kanban approach to varying the Quality of Service provided to certain backlog items. That's what a fast track lane is...a way of varying the quality of service that a Scrum team gives to certain items. When something hits a fast track lane, a well-trained team will swarm over it and decide who is best qualified to progress the matter. While they do this, their own tasks will be marked as impeded or blocked. Then, the decision made, all others return to their work in progress. So if fast track lanes are a widely understood and practical way of managing operational issues, what is wrong with them, Scrum-wise? The answer is that Scrum - unlike Lean Kanban - doesn't provide for variations in quality of service. Each piece of work is prioritized and negotiated into a Sprint backlog. The team then self-organizes to deliver a corresponding increment of functionality. The team will plan with the Product Owner what it intends to do during a sprint, and the sprint backlog they agree to belongs to them. No-one, not even the CEO of the organization, can override their sprint backlog by introducing work to be "fast tracked". The team wholly owns their sprint backlog. That's Scrum. When I point this out, teams can become crestfallen or even defensive. “What else are we supposed to do”, they say. “We aren’t dedicated 100% to doing project work. We still have support work to do, and serious issues always trump development. We have to fix them and put project work on hold.” My answer to that is that under the circumstances the team is facing, it may indeed be right to vary the quality of service by fast-tracking support work. It just isn’t Scrum, that’s all. It’s a type of "Scrumban", a Scrum variant that includes Kanban characteristics. This is no fault of the team, but it could suggest a problem higher up. Perhaps a dedicated Kanban support team hasn’t been properly resourced and trained so that Scrum development can proceed unimpeded. Perhaps the Product Owner is being undermined by other managers who have separate interests impacting the development. Whatever the situation, it needs to be made transparent and acknowledged by all stakeholders. So, the next step…and the one I’ll often indicate as the interview progresses…is to account for fast track work as impediments against product burndown or velocity. Moreover, these are impediments which are external to the team. It’s essentially a type of waste, or unplanned work, being generated from outside. It needs to be made quite transparent where this waste is coming from and what can be done to mitigate it. What can be done about those other teams, or workflows, or managers, who are undercutting this Scrum team’s ability to plan out their Sprints? Often, the source of these impediments will be the people interviewing me...and that’s when things can start to get really interesting!

With unit tests you can check that your code behaviours just as you expect it to. When writing your unit tests you shouldn't need to worry about if any other area of the application is working correctly. The benefits of unit testing are: Decouples your code Write more modular classes Functions are smaller and more focused Your functions are more defensive Quality of code becomes higher You will find it easier to reuse code. When writing unit tests you just need to test this one method of your application, if your method relies on another class/variable there should be a way you can inject this into the method. This is where dependency injection in your code comes in handy, it will allow you to inject objects into your classes to change the output of the class. There are a few things you need to do to make a method unit testable, methods will need an input from a parameter or a class variable and it will need a return or set a class variable in the method. If the method hasn't got these things then the method can not be unit testable. If there isn't a return of the method then there is no way in knowing how the method performs. Dependency Injection Dependency injection is when your object has a dependency on another object. The simplest form to understand what dependency injection is to think of a setter method. A setter method will take one parameter and set a class variable from this parameter. This is using code injection to pass in a parameter to be used as the class variable value. public function setValue( $val ) { $this->val = $val; } Without dependency injection this method will look like this. public function setValue() { $this->val = 10; } For unit testing you need to be aware of any classes that your class is dependent on. For example if you have a login class that will connect to a database. class login { private $db = false; public function __construct() { $this->db = new Database(); } public function loginUser( $user, $password ) { $this->db->checkLogin( $user, $password ); } } This login class has a dependency of the class Database in the constructor, which means that we can't unit test this correctly. If we want to unit test this then the database class has to be development and tested. If the database class is broken and we try to unit test the loginUser() method the test will always fail and we won't know that it's the database class which is broke or the loginUser() method that is broke. If the database class is finished development, tested and data is in the database then we can use this for the loginUser() function. But now our tests are dependent on data being correct in the database. If we pass in a username and password it must be in the database for our test to pass. Our code could be correct but if the data isn't there then our unit tests will fail. This isn't correct use of unit tests and is more suited to be an integration test. To fix this problem we can use dependency injection to pass in a database connector which will set the database class variable. There are 2 ways we can inject a variable into a class, it can either be in the constructor of the class or by using a setter method. I tend to use constructor for all required dependences and use the setter method if there is a default value for the class variable. class login { private $db = false; public function __construct( $db ) { $this->db = $db; } public function loginUser( $user, $password ) { $this->db->checkLogin( $user, $password ); } } Now this class isn't dependant on a certain database class we can pass in the database class by using the parameter on the login class constructor. We can unit test this loginUser() method by first setting the $this->db class variable. We don't want to rely on a real database as the data can change so we can either create a test harness database class or you can mock the database class. A test harness class will allow you to create your database class and hardcode any data that you need. In the example above we can create a method checkLogin(), in our test harness we can then hardcode a successful login username and password to make the loginUser() method pass. Or you can use a PHP mocking framework to mock a class/method/return value. Both methods have their benefits but mocking is normally quicker to code, but there are times when you want to hardcode certain variables in a class. Mocking Objects In TDD With PHP Mocking objects in test driven development allows you create objects to act as a certain class, if your test depends on another method to return a value, you can mock this method and make it return any value you want. In the example we used above you can mock the database class and choose what value we are expecting back from the checkLogin() method. When mocking a method you can choose what you want to return from this method, therefore we can write tests to see what will happen when checkLogin() returns TRUE and then we can write another test to see what happens when checkLogin() returns FALSE. Mocking objects means that you can run your unit tests without depending on another class returning the values you are expecting, ao you can test just your code in this one method. Here are some of the most popular PHP mocking frameworks: Mocking with PHPUnit - http://www.phpunit.de/manual/3.0/en/mock-objects.html Mocking with Phake - http://phake.digitalsandwich.com/docs/html/ Mocking with Mockery - https://github.com/padraic/mockery Mocking with Enchane PHP - https://github.com/Enhance-PHP/Enhance-PHP Mocking with FBMock - https://github.com/facebook/FBMock Dependency Injection With Interfaces If we are going to pass in a database connector in a constructor of the login class, then this database connector will always have to have a method of checkLogin(). This is why we should code our dependences by using interfaces to make sure that we are always passing in the correct type of class. class login { private $db = false; public function __construct( IDatabase $db ) { $this->db = $db; } } class database implements IDatabase { public function checkLogin( $username, $password ) { // check the login credentials } } interface IDatabase { public function checkLogin( $username, $password ); } This will make sure that the class we pass into the constructor is a type of IDatabase, so if our database class doesn't implement IDatabase then the code will fail and therefore our unit tests will fail. This means whatever we pass into the constructor we know that this class will be able to run the methods it needs for the unit tests to run.

I Programmer - Anime Scrum - An Overview If you are an anime fan, and perhaps even if you are not, then you might like a new poster about Scrum - in anime style. As long as you find anime cute or something then seeing the different people involved in the Scrum methodology as anime characters might help you convey the ideas to others. ... Scrum Primer - Scrum Overview - Anime version High-resolution versions of the overview: Scrum Overview - Blue Scrum Overview - Pink Scrum Overview - Green Feel free to use it in your own material. ..." Come on! You KNOW that's awesome! :)

In many Agile training programs and conferences, a common question that gets raised is, does Agile/Scrum work in maintenance projects? I always say "YES" and the team needs to tweak or invent the practices to suit their needs. Maintenance projects could be enhancement projects OR pure defect fixing projects. Enhancement projects involve new set of developments over existing one. Since the developers get a new set of requirements at the end of each iteration, one can apply the standard set of Scrum practices with little or no modification. Defect Fixing projects involve fixing defects on closed or current projects not in development. Sometimes these projects are boring, especially if a new team has been hired for defect fixing purposes only. The customer sends a set of defects on a daily basis or weekly basis with a deadline to deliver. The development team needs to fix them ASAP and send the patch for further testing. While coaching one of such a defect fixing projects, I found that the following Scrum practices can be applied without much modification: 1. Daily Scrum meetings 2. A Scrum of Scrum 3. Modeling days while solving complex defects 4. Information radiators displaying InProgress, completed, reopened, closed defects and other information 5. Usage of Wiki for collaborating with the customer 6. Requirement workshop while understanding complex defects 7. Review and Retrospective A common problem that I have found in defect fixing projects is setting the iteration length. Especially if the defects are given on a day to day basis without prior knowledge of what you are going to get, it makes the life of the development team bit difficult. This can be solved by collaborating with the customer and coming up with a plan to have 1 or 2 weeks of iteration length.

According to Steve McConnell in Code Complete (data from 1975-1992) most bugs don’t take long to fix. About 85% of errors can be fixed in less than a few hours. Some more can be fixed in a few hours to a few days. But the rest take longer, sometimes much longer – as I talked about in an earlier post. Given all of these factors and uncertainty, how to you estimate a bug fix? Or should you bother? Block out some time for bug fixing Some teams don’t estimate bug fixes upfront. Instead they allocate a block of time, some kind of buffer for bug fixing as a regular part of the team’s work, especially if they are working in time boxes. Developers come back with an estimate only if it looks like the fix will require a substantial change – after they’ve dug into the code and found out that the fix isn’t going to be easy, that it may require a redesign or require changes to complex or critical code that needs careful review and testing. Use a rule of thumb placeholder for each bug fix Another approach is to use a rough rule of thumb, a standard place holder for every bug fix. Estimate ½ day of development work for each bug, for example. According to this post on Stack Overflow the ½ day suggestion comes from Jeff Sutherland, one of the inventors of Scrum. This place holder should work for most bugs. If it takes a developer more than ½ day to come up with a fix, then they probably need help and people need to know anyways. Pick a place holder and use it for a while. If it seems too small or too big, change it. Iterate. You will always have bugs to fix. You might get better at fixing them over time, or they might get harder to find and fix once you’ve got past the obvious ones. Or you could use the data earlier from Capers Jones on how long it takes to fix a bug by the type of bug. A day or half day works well on average, especially since most bugs are coding bugs (on average 3 hours) or data bugs (6.5 hours). Even design bugs on average only take little more than a day to resolve. Collect some data – and use it Steve McConnell, In Software Estimation: Demystifying the Black Art says that it’s always better to use data than to guess. He suggests collecting time data for as little as a few weeks or maybe a couple of months on how long on average it takes to fix a bug, and use this as a guide for estimating bug fixes going forward. If you have enough defect data, you can be smarter about how to use it. If you are tracking bugs in a bug database like Jira, and if programmers are tracking how much time they spend on fixing each bug for billing or time accounting purposes (which you can also do in Jira), then you can mine the bug database for similar bugs and see how long they took to fix – and maybe get some ideas on how to fix the bug that you are working on by reviewing what other people did on other bugs before you. You can group different bugs into buckets (by size – small, medium, large, x-large – or type) and then come up with an average estimate, and maybe even a best case, worst case and most likely for each type. Use Benchmarks For a maintenance team (a sustaining engineering or break/fix team responsible for software repairs only), you could use industry productivity benchmarks to project how many bugs your team can handle. Capers Jones in Estimating Software Costs says that the average programmer (in the US, in 2009), can fix 8-10 bugs per month (of course, if you’re an above-average programmer working in Canada in 2012, you’ll have to set these numbers much higher). Inexperienced programmers can be expected to fix 6 a month, while experienced developers using good tools can fix up to 20 per month. If you’re focusing on fixing security vulnerabilities reported by a pen tester or a scan, check out the remediation statistical data that Denim Group has started to collect, to get an idea on how long it might take to fix a SQL injection bug or an XSS vulnerability. So, do you estimate bug fixes, or not? Because you can’t estimate how long it will take to fix a bug until you’ve figured out what’s wrong, and most of the work in fixing a bug involves figuring out what’s wrong, it doesn’t make sense to try to do an in-depth estimate of how long it will take to fix each bug as they come up. Using simple historical data, a benchmark, or even a rough guess place holder as a rule-of-thumb all seem to work just as well. Whatever you do, do it in the simplest and most efficient way possible, don’t waste time trying to get it perfect – and realize that you won’t always be able to depend on it. Remember the 10x rule – some outlier bugs can take up to 10x as long to find and fix than an average bug. And some bugs can’t be found or fixed at all – or at least not with the information that you have today. When you’re wrong (and sometimes you’re going to be wrong), you can be really wrong, and even careful estimating isn’t going to help. So stick with a simple, efficient approach, and be prepared when you hit a hard problem, because it's gonna happen.

Test-Driven Development is a code-level practice, based on running automated tests that are written before the production code they exercise. But practices can be applied only in the context where they were developed: when some premises are not present is difficult to apply TDD as-is. Automated specification For example, consider the premise of assertion automation: it is possible to write a (hopefully) small algorithm that is able to check the result of running production code and return true or false. In the case the problem is: Draw an antialiased circle on this blank canvas. -- Carlo Pescio it is not immediately clear how to define automated tests for this behavior. We could check that some pixels are still blank inside or outside the circle, or that there is a bound number of pixels of black color; or even that they are contiguous. An opinion I've heard (that I try not to misrepresent) is that we only need to write some looser tests in these cases, checking only a few pixels of the circle. This process will give us a little feedback on the API of our Canvas or Circle object, but not much on the algorithm we are implementing inside it. Are we going in the right direction? Have new test cases correctly been satisfied without a large intervention on the existing code? Are we painting some unrelated pixels due to an hidden bug? What I argument here is instead that we should change the nature of the feedback mechanism. Speaking in control theory terms, change the block that acquires the output and influences the input to our design process. Develop in the browser When I was developing a Couchapp, a kind of web application served directly from a CouchDB database, I was appaled by the difficulty of testing it. While the production code was composed of ~100 lines, it was a complex mix of technologies: HTML and CSS code, client-side JavaScript for managing user events and some server-side JavaScript for the "queries" (actually the server-side only consists of the database in Couchapps.) Some of this logic could be tested in automation, like the result of queries over views. Yet much of it was related to a user interface, and as such requiring a large time investment to automate. Instead of waking up my Selenium server and start to manipulate a browser with code, I noticed that this UI was almost read-only; there were a few cases where a new document would have to be inserted, but a manual test of them was short and did not even required to reload the page. The whole application state was observable. Summing it up, I performed a frequent manual test that took a few seconds instead of trying to define complex and brittle automation logic for testing the UI. Now that I've been introduced to a simple qualitative ROI model by Carlo Pescio's article, I would do the same for every context where: a large time investment is needed for automating tests. it is possible to perform manual tests quickly. as the only logic conclusion. A word of caution TDD has many benefits (including catching regressions early) so I'm not prepared to give it up just because it is difficult to test. These are technical scenarios where I have successfully followed TDD by the book: multithreaded and multiprocess code applications distributed over multiple machines computer vision (object recognition and tracking) image manipulation code (via comparison testing) development of browser bindings for Selenium And even in the case the big picture is not easy to test-first (like in the case of image manipulation), we can benefit from TDD the pieces of the solution. For example, in the computer vision case I wasn't able to write a test beforehand for tracking a car inside a movie. But I was able to TDD the objects that the algorithmic solution to the problem called for: Patch, Area, Cluster, Movement, and so on. End-to-end TDD is not always cheap but unit level TDD can often be, if it considers testability as a relevant property (while regression testing even at the end-to-end level is always possible, in the worst case with record and replay.) End-to-end specifications If we can't define automated assertions for our "big picture" problem, it doesn't mean that we cannot apply the TDD approach, by substituting a manual step. Going back to the circle problem, I would define manual test cases on an inspection page seen by a human. I've seen this done with layouts and multiple browsers to catch CSS rendering bugs, for example: It would be very difficult to check these screenshots automatically, as each browser renders pages a bit differently from the others. The iterative process becomes: Define a cheap manual test, automating the arrange and act phases but not the assertion. Write only the code necessary to make it pass. Refactor. As long as the number of tests does not increase without limit and the manual check can be performed quickly, this approach does not slow you down with respect to TDD by-the-book. You'll have to take care of regression with other means; but at least you define a set of manual test cases. Feedback! TDD is an instrument of feedback: if feedback cannot be gathered in an automated way, we have to resort to manual checking of the specifications. Here are other examples of manual tools for generating feedback: Read-Eval-Print Loops: you can experimenting with existing classes and functions, and easily repeat steps thanks to history. the browser refresh button: the fastest way to transform a PSD into an HTML and CSS template. MongoDB console for learning the database API; other kinds of consoles like Firebug and Chrome's, or Clojure's.

Everyone knows from reading The Mythical Man Month that as you add more people to a software development project you will see diminishing marginal returns. When you add a person to a team, there’s a short-term hit as the rest of the team slows down to bring the new team member up to speed and adjusts to working with another person, making sure that they fit in and can contribute. There’s also a long-term cost. More people means more people who need to talk to each other (n x n-1 / 2), which means more opportunities for misunderstandings and mistakes and misdirections and missed handoffs, more chances for disagreements and conflicts, more bottleneck points. As you continue to add people, the team needs to spend more time getting each new person up to speed and more time keeping everyone on the team in synch. Adding more people means that the team speeds up less and less, while people costs and communications costs and overhead costs keep going up. At some point negative returns set in – if you add more people, the team’s performance will decline and you will get less work done, not more. Diminishing Returns from any One Practice But adding too many people to a project isn’t the only case of diminishing returns in software development. If you work on a big enough project, or if you work in maintenance for long enough, you will run into problems of diminishing returns everywhere that you look. Pushing too hard in one direction, depending too much on any tool or practice, will eventually yield diminishing returns. This applies to: - Manual functional and acceptance testing - Test automation - Any single testing technique - Code reviews - Static analysis bug finding tools - Penetration tests and other security reviews Aiming for 100% code coverage on unit tests is a good example. Building a good automated regression safety net is important – as you wire in tests for key areas of the system, programmers get more confidence and can make more changes faster. How many tests are enough? In Continuous Delivery, Jez Humble and David Farley set 80% coverage as a target for each of automated unit testing, functional testing and acceptance testing. You could get by with lower coverage in many areas, higher coverage in core areas. You need enough tests to catch common and important mistakes. But beyond this point, more tests get more difficult to write, and find fewer problems. Unit testing can only find so many problems in the first place. In Code Complete, Steve McConnell explains that unit testing can only find between 15% and 50% (on average 30%) of the defects in your code. Rather than writing more unit tests, people’s time would be better spent on other approaches like exploratory system testing and code reviews or stress testing or fuzzing to find different kinds of errors. Too much of anything is bad, but too much whiskey is enough. Mark Twain, as quoted in Code Complete Refactoring is important for maintaining and improving the structure and readability of code over time. It is intended to be a supporting practice – to help make changes and fixes simpler and clearer and safer. When refactoring becomes an end in itself or turns into Obsessive Refactoring Disorder, it not only adds unnecessary costs as programmers waste time over trivial details and style issues, it can also add unnecessary risks and create conflict in a team. Make sure that refactoring is done in a disciplined way, and focus refactoring on those areas that need it the most: on code that is frequently changed, routines that are too big, too hard to read, too complex and error-prone. Putting most of your attention refactoring (or if necessary rewriting) this code will get you the highest returns. Less and Less over Time Diminishing returns also set in over time. The longer that you spend working the same way and with the same tools, the less benefits you will see. Even core practices that you’ve grown to depend on don’t pay back over time, and at some point may cost more than they are worth. It’s time again for New Year’s resolutions – time to sign up at a gym and start lifting weights. If you stick with the same routine for a couple of months, you will start to see good results. But after a while your body will get used to the work – if you keep doing the same things the same way your performance will plateau and you will stop seeing gains. You will get bored and stop going to the gym, which will leave more room for people like me. If you do keep going, trying to push harder for returns, you will overtrain and injure yourself. The same thing happens to software teams following the same practices, using the same tools. Some of this is due to inertia. Teams, organizations reach an equilibrium point and they want to stay there. Because it is comfortable, and it works – or at least they understand it. And because the better the team is working, the harder it is to get better – all the low-hanging fruit has been picked. People keep doing what worked for them in the past. They stop looking beyond their established routines, stop looking for new ideas. Competence and control lead to complacency and acceptance. Instead of trying to be as good as possible, they settle for being good enough. This is the point of inspect-and-adapt in Scrum and other time boxed methods – asking the team to regularly re-evaluate what they are doing and how they are doing it, what’s going well and what isn’t, what they should do more of or less of, challenging the status quo and finding new ways to move forward. But even the act of assessing and improving is subject to diminishing returns. If you are building software in 2-week time boxes, and you’ve been doing this for 3, 4 or 5 years, then how much meaningful feedback should you really expect from so many superficial reviews? After a while the team finds themselves going over the same issues and problems and coming up with the same results. Reviews become an unnecessary and empty ritual, another waste of time. The same thing happens with tools. When you first start using a static analysis bug checking tool for example, there’s a good chance that you will find some interesting problems that you didn’t know were in the code – maybe even more problems than you can deal with. But once you triage this and fix up the code and use the tool for a while, the tool will find fewer and fewer problems until it gets to the point where you are paying for insurance – it isn’t finding problems any more, but it might someday. In "Has secure software development reached its limits?” William Jackson argues that SDLCs – all of them – eventually reach a point of diminishing returns from a quality and security standpoint, and that Microsoft and Oracle and other big shops are already seeing diminishing returns from their SDLCs. Their software won’t get any better – all they can do is to keep spending time and money to stay where they are. The same thing happens with Agile methods like Scrum or XP – at some point you’ve squeezed everything that you can from this way or working, and the team’s performance will plateau. What can you do about diminishing returns? First, understand and expect returns to diminish over time. Watch for the signs, and factor this into your expectations – that even if you maintain discipline and keep spending on tools, you will get less and less return for your time and money. Watch for the team’s velocity to plateau or decline. Expect this to happen and be prepared to make changes, even force fundamental changes on the team. If the tools that you are using aren’t giving returns any more, then find new ones, or stop using them and see what happens. Keep reviewing how the team is working, but do these reviews differently: review less often, make the reviews more focused on specific problems, involve different people from inside and outside of the team. Use problems or mistakes as an opportunity to shake things up and challenge the status quo. Dig deep using Root Cause Analysis and challenge the team’s way of thinking and working, look for something better. Don’t settle for simple answers or incremental improvements. Remember the 80/20 rule. Most of your problems will happen in the same small number of areas, from a small number of common causes. And most of your gains will come from a few initiatives. Change the team’s driving focus and key metrics, set new bars. Use Lean methods and Lean Thinking to identify and eliminate bottlenecks, delays and inefficiencies. Look at the controls and tests and checks that you have added over time, question whether you still need them, or find steps and checks that can be combined or automated or simplified. Focus on reducing cycle time and eliminating waste until you have squeezed out what you can. Then change your focus to quality and eliminating bugs, or to simplifying the release and deployment pipeline, or some other new focus that will push the team to improve in a meaningful way. And keep doing this and pushing until you see the team slowing down and results declining. Then start again, and push the team to improve again along another dimension. Keep watching, keep changing, keep moving ahead. Source: http://swreflections.blogspot.com/2011/11/diminishing-returns-in-software.html

I’ve been going over a couple of posts by Steve Kilner that question whether Agile methods can be used effectively in software maintenance. It’s a surprising question really. There are a lot of maintenance teams who have had success following Agile methods like Scrum and Extreme Programming (XP) for some time now. We’ve been doing it for almost 5 years, enhancing and maintaining and supporting enterprise systems, and I know that it works. Agile development naturally leads into maintenance – the goal of incremental Agile development is to get working software out to customers as soon as possible, and get customers using it. At some point, when customers are relying on the software to get real business done and need support and help to keep the system running, teams cross from development over to maintenance. But there’s no reason for Agile development teams to fundamentally change the way that they work when this happens. It is harder to introduce Agile practices into a legacy maintenance team – there are a lot of technical requirements and some cultural changes that need to be made. But most maintenance teams have little to lose and lots to gain from borrowing from what Agile development teams are doing. Agile methods are designed to help small teams deal with a lot of change and uncertainty, and to deliver software quickly – all things that are at least as important in maintenance as they are in development. Technical practices in Extreme Programming especially help ensure that the code is always working – which is even more important in maintenance than it is in development, because the code has to work the first time in production. Agile methods have to be adapted to maintenance, but most teams have found it necessary to adapt these methods to fit their situations anyways. Let’s look at what works and what has to be changed to make Agile methods like Scrum and XP work in maintenance. What works well and what doesn’t Planning Game Managing maintenance isn’t the same as managing a development project – even an Agile development project. Although Agile development teams expect to deal with ambiguity and constant change, maintenance teams need to be even more flexible and responsive, to manage conflicts and unpredictable resourcing problems. Work has to be continuously reviewed and prioritized as it comes in – the customer can’t wait for 2 weeks for you to look at a production bug. The team needs a fast path for urgent changes and especially for hot fixes. You have to be prepared for support demands and interruptions. Structure the team so that some people can take care of second-level support, firefighting and emergency bug fixing and the rest of the team can keep moving forward and get something done. Build slack into schedules to allow for last-minute changes and support escalation. You will also have to be more careful in planning out maintenance work, to take into account technical and operational dependencies and constraints and risks. You’re working in the real world now, not the virtual reality of a project. Standups Standups play an important role in Agile projects to help teams come up to speed and bond. But most maintenance teams work fine without standups – since a lot of maintenance work can be done by one person working on their own, team members don’t need to listen to each other each morning talking about what they did yesterday and what they’re going to do – unless the team is working together on major changes. If someone has a question or runs into a problem, they can ask for help without waiting until the next day. Small releases Most changes and fixes that maintenance teams need to make are small, and there is almost always pressure from the business to get the code out as soon as it is ready, so an Agile approach with small and frequent releases makes a lot of sense. If the time boxes are short enough, the customer is less likely to interrupt and re-prioritize work in progress – most businesses can wait a few days or a couple of weeks to get something changed. Time boxing gives teams a way to control and structure their work, an opportunity to batch up related work to reduce development and testing costs, and natural opportunities to add in security controls and reviews and other gates. It also makes maintenance work more like a project, giving the team a chance to set goals and to see something get done. But time boxing comes with overhead – the planning and setup at the start, then deployment and reviews at the end – all of which adds up over time. Maintenance teams need to be ruthless with ceremonies and meetings, pare them down, keep only what’s necessary and what works. It’s even more important in maintenance than in development to remember that the goal is to deliver working code at the end of each time box. If some code is not working, or you’re not sure if it is working, then extend the deadline, back some of the changes out, or pull the plug on this release and start over. Don’t risk a production failure in order to hit an arbitrary deadline. If the team is having problems fitting work into time boxes, then stop and figure out what you’re doing wrong – the team is trying to do too much too fast, or the code is too unstable, or people don’t understand the code enough – and fix it and move on. Reviews and Retrospectives Retrospectives are important in maintenance to keep the team moving forward, to find better ways of working, and to solve problems. But like many practices, regular reviews reach a point of diminishing returns over time – people end up going through the motions. Once the team is setup, reviews don’t need to be done in each iteration unless the team runs into problems. Schedule reviews when you or the team need them. Collect data on how the team is working, on cycle time and bug report/fix ratios, correlate problems in production with changes, and get the team together to review if the numbers move off track. If the team runs into a serious problem like a major production failure, then get to the bottom of it through Root Cause Analysis. Sustainable pace / 40-hour week It’s not always possible to work a 40-hour week in maintenance. There are times when the team will be pushed to make urgent changes, spend late nights firefighting, releasing after hours and testing on weekends. But if this happens too often or goes on too long the team will burn out. It’s critical to establish a sustainable pace over the long term, to treat people fairly and give them a chance to do a good job. Pairing Pairing is hard to do in small teams where people are working on many different things. Pairing does make sense in some cases – people naturally pair-up when trying to debug a nasty problem or walking through a complicated change – but it’s not necessary to force it on people, and there are good reasons not to. Some teams (like mine) rely more on code reviews instead of pairing, or try to get developers to pair when first looking at a problem or change, and at the end again to review the code and tests. The important thing is to ensure that changes get looked at by at least one other person if possible, however this gets done. Collective Code Ownership Because maintenance teams are usually small and have to deal with a lot of different kinds of work, sooner or later different people will end up working on different parts of the code. It’s necessary, and it’s a good thing because people get a chance to learn more about the system and work with different technologies and on different problems. But there’s still a place for specialists in maintenance. You want the people who know the code the best to make emergency fixes or high-risk changes – or at least have them review the changes – because it has to work the first time. And sometimes you have no choice – sometimes there is only one person who understands a framework or language or technical problem well enough to get something done. Coding Guidelines – follow the rules Getting the team to follow coding guidelines is important in maintenance to help ensure the consistency and integrity of the code base over time – and to help ensure software security. Of course teams may have to compromise on coding standards and style conventions, depending on what they have inherited in the code base; and teams that maintain multiple systems will have to follow different guidelines for each system. Metaphor In XP, teams are supposed to share a Metaphor: a simple high-level expression of the system architecture (the system is a production line, or a bill of materials) and common names and patterns that can be used to describe the system. It’s a fuzzy concept at best, a weak substitute for more detailed architecture or design, and it’s not of much practical value in maintenance. Maintenance teams have to work with the architecture and patterns that are already in place in the system. What is important is making sure that the team has a common understanding of these patterns and the basic architecture so that the integrity isn’t lost – if it hasn’t been lost already. Getting the team together and reviewing the architecture, or reverse-engineering it, making sure that they all agree on it and documenting it in a simple way is important especially when taking over maintenance of a new system and when you are planning major changes. Simple Design Agile development teams start with simple designs and try to keep them simple. Maintenance teams have to work with whatever design and architecture that they inherit, which can be overwhelmingly complex, especially in bigger and older systems. But the driving principle should still be to design changes and new features as simple as the existing system lets you – and to simplify the system’s design further whenever you can. Especially when making small changes, simple, just-enough design is good – it means less documentation and less time and less cost. But maintenance teams need to be more risk adverse than development teams – even small mistakes can break compatibility or cause a run-time failure or open a security hole. This means that maintainers can’t be as iterative and free to take chances, and they need to spend more time upfront doing analysis, understanding the existing design and working through dependencies, as well as reviewing and testing their changes for regressions afterwards. Refactoring Refactoring takes on a lot of importance in maintenance. Every time a developer makes a change or fix they should consider how much refactoring work they should do and can do to make the code and design clearer and simpler, and to pay off technical debt. What and how much to refactor depends on what kind of work they are doing (making a well-thought-out isolated change, or doing shotgun surgery, or pushing out an emergency hot fix) and the time and risks involved, how well they understand the code, how good their tools are (development IDEs for Java and .NET at least have good built-in tools that make many refactorings simple and safe) and what kind of safety net they have in place to catch mistakes – automated tests, code reviews, static analysis. Some maintenance teams don’t refactor because they are too afraid of making mistakes. It’s a vicious circle – over time the code will get harder and harder to understand and change, and they will have more reasons to be more afraid. Others claim that a maintenance team is not working correctly if they don’t spend at least 50% of their time refactoring. The real answer is somewhere in between – enough refactoring to make changes and fixes safe. There are cases where extensive refactoring, restructuring or rewriting code is the right thing to do. Some code is too dangerous to change or too full of bugs to leave the way it is – studies show that in most systems, especially big systems, 80% of the bugs can cluster in 20% of the code. Restructuring or rewriting this code can pay off quickly, reducing problems in production, and significantly reducing the time needed to make changes and test them as you go forward. Continuous Testing Testing is even more important and necessary in maintenance than it is in development. And it’s a major part of maintenance costs. Most maintenance teams rely on developers to test their own changes and fixes by hand to make sure that the change worked and that they didn’t break anything as a side effect. Of course this makes testing expensive and inefficient and it limits how much work the team can do. In order to move fast, to make incremental changes and refactoring safe, the team needs a better safety net, by automating unit and functional tests and acceptance tests. It can take a long time to put in test scaffolding and tools and write a good set of automated tests. But even a simple test framework and a small set of core fat tests can pay back quickly in maintenance, because a lot changes (and bugs) tend to be concentrated in the same parts of the code – the same features, framework code and APIs get changed over and over again, and will need to be tested over and over again. You can start small, get these tests running quickly and reliably and get the team to rely on them, fill in the gaps with manual tests and reviews, and then fill out the tests over time. Once you have a basic test framework in place, developers can take advantage of TFD/TDD especially for bug fixes – the fix has to be tested anyways, so why not write the test first and make sure that you fixed what you were supposed to? Continuous Integration To get Continuous Testing to work, you need a Continuous Integration environment. Understanding, automating and streamlining the build and getting the CI server up and running and wiring in tests and static analysis checks and reporting can take a lot of work in an enterprise system, especially if you have to deal with multiple languages and platforms and dependencies between systems. But doing this work is also the foundation for simplifying release and deployment – frequent short releases means that release and deployment has to be made as simple as possible. Onsite Customer / Product Owner Working closely with the customer to make sure that the team is delivering what the customer needs when the customer needs it is as important in maintenance as it is in developing a new system. Getting a talented and committed Customer engaged is hard enough on a high-profile development project – but it’s even harder in maintenance. You may end up with too many customers with conflicting agendas competing for the team’s attention, or nobody who has the time or ability to answer questions and make decisions. Maintenance teams often have to make compromises and help fill in this role on their own. But it doesn’t all fit…. Kilner’s main point of concern isn’t really with Agile methods in maintenance. It’s with incremental design and development in general – that some work doesn’t fit nicely into short time boxes. Short iterations might work ok for bug fixes and small enhancements (they do), but sometimes you need to make bigger changes that have lots of dependencies. He argues that while Agile teams building new systems can stub out incomplete work and keep going in steps, maintenance teams have to get everything working all at once – it’s all or nothing. It’s not easy to see how big changes can be broken down into small steps that can be fit into short time boxes. I agree that this is harder in maintenance because you have to be more careful in understanding and untangling dependencies before you make changes, and you have to be more careful not to break things. The code and design will sometimes fight the kinds of changes that you need to make, because you need to do something that was never anticipated in the original design, or whatever design there was has been lost over time and any kind of change is hard to make. It’s not easy – but teams solve these problems all the time. You can use tools to figure out how much of a dependency mess you have in the code and what kind of changes you need to make to get out of this mess. If you are going to spend “weeks, months, or even years” to make changes to a system, then it makes sense to take time upfront to understand and break down build dependencies and isolate run-time dependencies, and put in test scaffolding and tests to protect the team from making mistakes as they go along. All of this can be done in time boxed steps. Just because you are following time boxes and simple, incremental design doesn’t mean that you start making changes without thinking them through. Read Working With Legacy Code – Michael Feathers walks through how to deal with these problems in detail, in both object oriented and procedural languages. What to do if it takes forever to make a change. How to break dependencies. How to find interception points and pinch points. How to find structure in the design and the code. What tests to write and how to get automated tests to work. Changing data in a production system, especially data shared with other systems, isn’t easy either. You need to plan out API changes and data structure changes as carefully as possible, but you can still make data and database changes in small, structured steps. To make code changes in steps you can use Branching by Abstraction where it makes sense (like making back-end changes) and you can protect customers from changes through Feature Flags and Dark Launching like Facebook and Twitter and Flickr do to continuously roll out changes – although you need to be careful, because if taken too far these practices can make code more fragile and harder to work with. Agile development teams follow incremental design and development to help them discover an optimal solution through trial-and-error. Maintenance teams work this way for a different reason – to manage technical risks by breaking big changes down and making small bets instead of big ones. Working this way means that you have to put in scaffolding (and remember to take it out afterwards) and plan out intermediate steps and review and test everything as you make each change. Sometimes it might feel like you are running in place, that it is taking longer and costing more. But getting there in small steps is much safer, and gives you a lot more control. Teams working on large legacy code bases and old technology platforms will have a harder time taking on these ideas and succeeding with them. But that doesn’t mean that they won’t work. Yes, you can be Agile in maintenance.

The Goal by Eli Goldratt is a business book in the form of a novel, where the protagonist must save his factory from closing due to very low productivity. The Goal is not limited to the management of a large organization (not even to for-profit companies): you simply have to define different units of measurement, like goal units instead of making money, the default goal. In fact, from the applications of the Theory of Constraints in our field I think it applies to software development too. What follows is my translation of the themes of The Goal to our field. The Goal is... Mking money, of course. For the ones of you with knowledge in accounting, the original goal is: raising throghput, the amounts of items sold (not produced) in the unit of time. Lowering investment/inventory, all the money tied up in the system in the form of assets that could be sold or products that stay in a warehouse. Lowering operational expense, all the money that we have spent as support and that cannot be recovered. How does these measurements apply to software development? A team does not always have an impact on contract negotiation, so often talking about money is far from everyday reality (kudos to you if you can apply that point of the Agile Manifesto.) The goal for software development can be translated, in my opinion, to: raising the throughput, the amount of features delivered (deployed, not implemented or tested) in the unit of time. You can measure this amount in story points, since feature vary in size. lowering investment/inventory, all the time tied up in the system in the form of undeployed or untested features that clutter the code base. In a minor part, also investment in the form of hardware, but that's by far less important than the team's time. lower operational expense, the time spent by developers every day in order to support the development. Automation is a kind of time investment that will bring more time (and quality) in the future, lowering operational expense. Like for material products, WIP has storage and opportunity costs which goes into the operational expense. Kanban is a tool that tries to reduce WIP in order to foster the two latter points. Throughput accounting This kind of throughput accounting is emphasized in the novel, over the use of cost accounting, where each developer (ehm, factory worker) has to be occupied all the time, even if the work he is doing isn't moving towards the goal: neverending refactoring. Specification of a feature which cannot be implemented until two months are gone, and will have to be rewritten. Implementation of features which won't be merged with the main branch any time soon. With Test-Driven Development, we are getting good at moving a feature from implemented to tested directly in the same commit. Yet the missing step is getting the feature to the users: maybe that's also what Continuous Deployment is all about... Dependent events Dependent events and statistical fluctuations are production systems topics that make a balanced plant close to bankruptcy: however, we're not at the point in which we can model our team as precisely as a factory. The basic point is that a plant in which everyone is working all the time is inefficient: when an early stage (like defining a specification or implementing a feature) gets delayed, downstream step such as deployment are dalayed too. Converely, when an upstream step finish earlier, the downstream stage is already at maximum efficiency and cannot process the intermediate result faster. I wonder if this applies to software development too. In a factory, workers are specialized and can do just a few jobs across the plant. Since workers and machines have different production rates, there will be just one bottleneck: the slowest one. If products have to pass from the bottleneck, anyone producing faster than the bottleneck will just accumulate WIP in front of him. Continuing with our example, if the analyst or domain expert is churning out specifications for new features every day, most of them are just WIP in front of the development team. Once there is an established buffer, any additional specification won't raise throughput any faster; instead, it will raise the inventory (partial features) and the time spent in managing it. I think this is not always true in the most technical phases of development instead. For example, in a small team a developer may be moved to testing or refactoring, or setting up Continuous Integration or evaluation of a new library. Unless you have a DBA which can just manage databases, your developer is not fixed into a stage of the system. The bottleneck The previous example featured a bottleneck, the most famous concept of the Theory of Constraints introduced in the book. This translation to the software development case is mine and could be incomplete. A feature (or a user story) has to pass in a series of stations where different people will work on it to make it real: specification from a domain expert, implementation from a technical team, extended testing with optional customer validation, deployment which should be fast but at the same time must not kill the current version of the application. Each station has an average velocity. By definition, there is a station which is the slowest and can process fewest story points in the unit of time. This is the bottleneck. (This is not always true, as velocity may vary greatly in time with the addition of new people or hidden lines discovered in a feature. Becoming good at estimation and stabilizing a team are two objectives that help reach the assumption.) You can identify a bottleneck by looking at where is the WIP: it will accumulate in front of it. If you already have a kanban board, this phase is simpler... Once identified, the throughput of the system can only be improved by raising the bottleneck's capacity enough so that it is no more a bottleneck. You can move people to it (keeping an eye on communication costs); ensure it is used at maximum efficiency by freeing the specialized developers from other mundane tasks. Now you can restart and find a new bottleneck... Conclusions This is just a little introduction to the themes of the Theory of Constraints and Goldratt's teachings; I don't pretend to explain the whole book in an article. It is also against the Socratic method: you should reach the answers yourself, and these are just examples from my experience. There is more to Goldratt and The Goal than bottlenecks and throughput, such as continuous improvement. If you're working or managing in a software development team, I suggest you to read this book if you have the opportunity. Even when freelancing, it is an eye-opener in moving towards a Goal instead of busyworking; and it's written with a never boring teaching method.

What happens when you have no product owner at all? How does a team know what features to develop in what order? Several teams I know encountered this. They all had product managers. Most of them had BAs. All of them had a technical manager who was willing to be their product owner, but they had no real product owner. They called themselves Scrum-but. I used to think this was ok. I now think Scrum-but is a bad label. That’s because agile needs a responsible person who is not part of the cross-functional technical team to rank the backlog so the team knows the order of the work. Without that person, the team does not know what to do. So why is it so bad for a team to call itself Scrum-but? Because it’s not Scrum-but. It’s not Scrum. It’s iterative and incremental, but it’s not even close to Scrum. It’s not agile. Johanna's General Agile Picture When you have no product owner who is not outside the team, or outside the hierarchy of the team, you lose something very precious to agility, the notion of the customer or customer surrogate. You lose the person who could be helping the team understand what the customer really wants. You lose the back-and-forth about the product that the customer helps the team understand. The manager can help the team understand the requirements, but the manager is not the customer. The manager is not the person who can set the real acceptance criteria. The manager can see the demo, but the manager cannot say for sure that the team is developing the correct requirements in the correct order. So why am I so insistent that we stop calling this Scrum-but, and even stop calling this agile? Because it breaks down the separation when-and-what-to-build (responsible person responsibility from ongoing incremental delivery of product on a regular basis (the cross-functional team responsibility). The customer or responsible person explains when-to-build in my little picture. The team decides how to build it. When the team manager gets involved, that allows the “business” to be unaccountable for developing the system. How do you know what is shippable product without the responsible person? The problem is this: System development, product development is a joint venture between the business people and the technical people. We need the legal, marketing, sales, and anyone else on the “business” side of the house to help us with the what-and-when to build decisions. That’s why we need a responsible person. In Scrum, that person is called a product owner. And, we need a technical project team to deliver the value. We use agile as an approach and use the demo because it shows business value every iteration. When the business is unaccountable, the agile ecosystem breaks down. We no longer have ideas coming into that funnel, being evaluated by that responsible person. Sure that responsible person has a lot to do. And, that responsible person should develop product roadmaps and make the potential product direction transparent to the rest of the organization. That way, the next iteration or two is clear for the team, and everyone can fight discuss the product direction. But when all the discussion is in the technical organization, those discussions tend to not happen. Or the discussions go off in a different direction than the product needs to go. And, that’s a Very Bad Thing. Because, when the discussions don’t occur, the technical group takes all the responsibility for the product: for what to build, when to build it, and for how to build it. And that means we have let the rest of the business abdicate all of their responsibility for their part of the product. That’s not the partnership agile promises us, nor is the transparency agile promises us. So, when you hear Scrum-but because you have no product owner, substitute “On the road to agile.” You’re actually iterative and incremental, but not agile. You have not made one of the necessary cultural changes for transitioning to agile. Can you keep doing what you are doing? Sure, if it’s working for you. And, that’s the million dollar question: How is this working for you? (If you would like more hints as to what else to do, consider my project management book, Manage It! Your Guide to Modern, Pragmatic Project Management. You have other options, if you cannot manage the agile cultural change right now. Those other options will help you move closer to agile than trying Scrum-but and failing.) This is one of the points—the agile ecosystem and making it succeed—I’m working on for my keynote at the Agile Vancouver conference in late October.

This is what Wikipedia writes about the watermelon: The Watermelon (Citrullus lanatus (Thunb.), family Cucurbitaceae) can be both the fruit and the plant of a vine-like (scrambler and trailer) plant originally from southern Africa, and is one of the most common types of melon. [...] The watermelon fruit, loosely considered a type of melon (although not in the genus Cucumis), has a smooth exterior rind (green, yellow and sometimes white) and a juicy, sweet interior flesh (usually pink, but sometimes orange, yellow, red and sometimes green if not ripe). Watermelon (Citrullus lanatus (Thunb.), family Cucurbitaceae) can be both the fruit and the plant of a vine-like (scrambler and trailer) plant originally from southern Africa, and is one of the most common types of melon. This flowering plant produces a special type of fruit known by botanists as a pepo, a berry which has a thick rind (exocarp) and fleshy center (mesocarp and endocarp); pepos are derived from an inferior ovary, and are characteristic of the Cucurbitaceae. The watermelon fruit, loosely considered a type of melon (although not in the genus Cucumis), has a smooth exterior rind (green, yellow and sometimes white) and a juicy, sweet interior flesh (usually pink, but sometimes orange, yellow, red and sometimes green if not ripe). For my metaphor, I’ll use the one with red flesh but orange and yellow would work too. I think most of us experienced the phenomenon when the project status is red but is getting greener and greener when climbing the management ladder. The project’s core is red but for the management it has a nice green paring, so it looks like a watermelon. This is why I call this phenomenon Watermelon Reporting. But why are we creating such reports and how can we avoid it? Why? The bearer of bad news already had a bad time in the ancient world. If he was lucky, they gave him the chop but in other cases they simply chopped his head of. This hasn’t changed until now but fortunately only in a figurative sense. Some bosses aren’t interested that there are problems with a project in their responsibility because if they know about it, they are in charge. So what do they do to avoid incurring the wrath of their boss ? They tweak the project status just a bit and the melon starts growing. Another reason could be that nobody wants to be in the focus of management, thus they embellish the project status in the hope that everything turns for the better. And as we all know hope is the last to die. In the end the result is the same.. Eventually the overripe melon bursts and there is no rescue for the project anymore. How to avoid it? The answer is easy: Transparency, transparency and transparency. If there is no way to hide the current status the watermelon can’t grow. Fortunately Scrum and other agile frameworks provide tools like burndown charts and backlogs to help the team with their transparency. But there are also tools like dashboards or kanban boards to do this job, but this will be the subject of one of my next blog posts. Conclusion The nuts and bolts of any project are transparency. If the project status is transparent, the watermelons can’t arise. If anybody is able to get the information, it will be difficult to hide something.

I recently attended the Agile Coach Gathering UK in Bletchley Park near London. I met a lot of interesting people, had some great talks and discussion and learned a ton. As the gathering was an open space conference I also proposed a session with the topic “What’s your favorite Agile Game?”. The goal was to collect some great games I could play in my next Scrum or Kanban trainings. A fun fact of this session was that everybody found out that we knew more games than we expected before. We came up with the following list of games. P&Q P&Q is not really a game but a collaborative process. The P&Q is a simple process which makes just two things; “P’s” and “Q’s.” The objective of the exercise is to make a decision as to how to best maximize the profit of this process. A more precise description can be found here. The XP Game The XP Game if one of the oldest and most known games in the agile community. The XP Game is a playful way to familiarize the players with some of the more difficult concepts of the XP Planning Game, like velocity, story estimation, yesterday’s weather and the cycle of life. A detailed description of the game can be found here. There are several variations of the game but my personal favorite is the LEGO(c) XP Game. I’m a big LEGO(c) fan and use any excuse to play with those bricks. Here are some photos of a team playing this game. I highly recommend this game to any team new to agile. Scrum from hell Scrum from hell is more a role play than a game and simulates a dysfunctional daily scrum meeting. It is always fun observing the participants playing the roles. The duration of this game is only 15 minutes and a must for any Scrum training. A description of this game can be found here. The communication game As I don’t have the real name of this game I just named it this way. This game is all about communication. The following roles are part of this game: Client Business Analyst Architect Developer In the first round nobody is allowed to speak. The client describes his requirements as written document to the business analyst and the BA passes on what he did understand to his architect and finally to the developer. As the info arrives at the developer he starts to build what he understood. When he is ready the review starts. In most cases the client don’t get what he has expected. In the second round everybody is allowed to speak and ask questions. In most cases this leads to a product the client asked for. If you have some more info about this game or even some artifacts, leave a comment. The ballpoint game This game is also one of my favorites. It’s about passing as many balls as possible between the players during a given time. With this game the concept of iterations/sprints and retrospective are explained. I already posted a more detailed description of this game in my blog which can be found here. Making paper hats In this game the concepts of velocity and iteration/sprint are explained. The main goal is to map the planned amount of paper hats with the actual amount. This game can also be played by blowing balloons or any other simple task. The customer in this game tries to push the development team to build as many paper hats as possible during an iteration. The result is that most of the build paper hats are useless as the quality is quite low. The customer keeps pushing until the team realizes that they are only able to build x paper hats during one iteration in the requested quality. Now the team knows his own velocity and is able to negotiate with the customer on the maximum number of paper hat. Another outcome of this game is that the player realize that quality is not negotiable. If someone has a link to a more precise description, please leave a comment. Other games There are a lot of agile games online. During the session I suggested the following places to search for additional games: http://www.tastycupcakes.com – This is a great resource for agile games. I highly recommend to have look here http://www.kanbangames.net – On this page you’ll find some games explaining the concepts of kanban and lean software development. AgileGames on Google Groups – If you’re interested in the newest games or want to discuss about games, this is the place to go. Come and join our group. If you have any other resources or any other addition to this blog post, feel free to leave a comment.

The Agile Chronicles is a set of articles documenting my experiences using an Agile process (Scrum) in software development on my current Flex project. Part 1 – Stressful Part 2 – Code Refactoring Part 3 – Branch Workflow Part 4 – POC, Strategy, and Design Challenges Part 5 – Acceptance Criteria & Punting Part 6 – Tools, Extra Merge Day, and Postponed Transitions Part 7 – Bugs, Unit Testing, and Throughput Part 8 – Demo, Burnout, and Feature Juggling Part 9 – Scope Creep Part 10 – Conclusions This entry is about defining what the acceptance criteria for user stories are so you can confirm you really did complete them during the UAT at the end of the sprint. It’s also about determining when you should abort a task that is taking too much time. Acceptance Criteria – Is the User Story Really Done? Each of our 2 week sprints include a set of user stories each developer must complete by the end of the sprint. We naturally overload our selves to ensure we have an added sense of urgency, additional user stories to tackle of we encounter a major roadblock to completing a particular story, and to clearly articulate what is priority in a bigger picture. On the 2nd Friday, we do our UAT (User Acceptance Testing). The way we do it is go through the latest build from SVN’s trunk, and collectively try to do each of the user stories. Like, “User story #32 says, ‘The user can type in their user name and password, and if they are a registered user, they will be taken to the main screen’”. If this happens, that user story is complete, we get confirmation from the client as such, and move to the next. It’s not always that black and white, though. Some user stories, even if still simple, have certain acceptance criteria associated with them. The first, and only, acceptance criteria we used in the beginning was what I just described; clearly written user stories that are easily testable. As the application grows in complexity, and certain things are assumed to go along with the user story, we’ve had to add some acceptance criteria to certain user stories. For example, even though in Sprint #1 I did in fact get the login working, none of the fonts were correct, and the alignment on some of the graphics were off. This was obvious to all, including me. Did this mean that I still completed the user story? Yes. “Yes” according to who? The client. Therefore, user story acceptance criteria seems client driven. Some clients, those not like the kind agencies have, are more functionality oriented and they don’t notice subtle design inconsistencies all the time like Verdana vs. Helvetica LT 57 Condensed. Others are more about design, and less about functionality. Some are both. Our particular client is more on the functionality side of the fence because we are working with them to complete functionality; it’s not just us working alone. That said, it still seems like each user story has it’s own unique acceptance criteria. This isn’t always easy to do, either. You really need to announce the assumptions because if you don’t, one thing you can count on is the following Friday’s UAT pointing them out; either they were assumed, and are in there, or weren’t, and aren’t. Sometimes you don’t know what those assumptions are, and thus, yet again one of the validations of using iterative development in short sprints; getting the functionality done quickly and in front of the client so they can see those assumptions. Regardless, this is something our project manager and client have been doing every Monday, both during and after, our planning session. Adding more implied functionality to a user story implies it could be more challenging, thus more work involved, and thus worth more points. This in turn affects how many user stories should be tackled per sprint. Punt – You’re in a downward spiral, pull up! You ever attempt to code something really hard, and not quite get there? Or worse, you keep getting close, yet every step feels like you’re only getting farther away as you start running out of ideas… or you have less time to implement your new ideas? This is what I call the downward spiral, akin to an airplane which stalled from going too high too quickly, and now is in a downward spiral. It can happen to those who compensate for lack of intelligence with willpower (me) and those who are smart, get in the zone, and never come out. I did that this sprint, badly. I had a really challenging component, a sub-task in a user story, and greatly underestimated my ability to pull it off. As the days wore on, my determination only increased. Two times I had a “flashback” to my Flash days, and thought about ways of faking it as well as having a plan B. Upon taking 10 minutes to test my Plan B three days in, I realized my Plan B wasn’t going to work either. I then started to do the math, and figure out how many days I had left in the Sprint, and how many user stories I had left to complete in those days. I was over what I should of been. In short, I was screwed. There is an old investment lesson called “cutting your losses”. It’s about recognizing that your investment in a particular company or mutual fund is bad. The company could be blatantly going downhill, and you can’t sell short for whatever reason (selling a hammer to someone, and then buying it back for less than you sold it for). So, the only option is to pull your money out before you lose more money. It’s the right business decision to do. The analogy is an alligator has bitten your arm. You can run, and lose your arm, or attempt to kick it, and hope it opens its mouth long enough for you to pull your arm out. This is usually destined to be bad because you could then lose your leg… or worse. Same with bad investments. If they are bad, pull your money out. The only thing you have to show for it is the money you saved. Same with aborting coding a component you won’t complete in a reasonable time frame. By stopping your aren’t giving up. People like me take it very personally, and perceive it as giving up. Jesse Warden doesn’t give up. I really have to change my mindset that, given enough time, I could complete it. However, there are more important things left to be done, so I put it “on hold”. Whatever bs you tell yourself to pull up out of the downward spiral will do. This is what I call punting. Punting is a play that you do in American football. If you’re on offense, your goal is to carry the ball into the opponents end zone. If you don’t get far enough after your allotted 4 downs, you’re going to be in trouble if the opponent gets the ball, and is closer to your end zone than you their’s. So, you punt; kick the ball as far as you can down towards their end zone, yet not on it, in the hopes you make them travel as far as possible back to your end zone. If for whatever reason, your team cannot take the ball to the opponents end zone by 3rd down, on the 4th, you need to make the decision: Do you go for it or do you not take the risk and end up screwing yourself if you don’t make it, and punt instead? In American Football, this can be a very complicated decision. In Agile software, not so much. Do the math; how many days / hours do you have to play with to hit the rest of your user stories? Is it worth it to you to work 12 hour days just in case your risk doesn’t pay off? It is it worth it to work 12 hour days EVEN IF you end up failing? I did the math too late this time, but won’t make that same mistake again. That’s what I’ll keep telling myself as I work this Saturday on Thanksgiving weekend, and next week as I work 12 hour days.

In this multi-part post, I’m going to share my personal experiences while working with user stories for gathering, tracking and planning requirements. It currently consists out of three parts: What are they and why do you need them? Who writes them and how do you control scope? Where do you start and what about the planning? You can also download all parts as one comprehensive PDF for easy printing or e-reading. Where do you start? Suppose that after intensive discussions and tough scoping sessions you ended up with a list of user stories and are about to start building the system. The first story not only needs to realize some particular feature, but also involves building a skeleton implementation of the system’s architecture. How do you avoid spending way too much time on plumbing and other general purpose stuff you need for the rest of the stories? The article Managing the Bootstrap Story by Jennitta Andrea addressed this challenge in more detail and offers some alternative solutions. One of these solutions is to find and define a user story with the product owner that offers minimal functionality yet still has project value. Such a story is often referred to as the backbone story because you realize the backbone of your system in it. It’s quite common to use the backbone story to realize a proof-of-concept (PoC) that verifies the chosen architecture. Since a working PoC can give the product owner confidence that the team is able to build such a product, that fact alone may be enough project value for the product owner. More storyotypes? You might have suspected it already, but that backbone story is just an example of another storyotype. In fact, after I started looking for an approach to capture the non-functional requirements of a project or system, I ran into a slide deck that mentioned a whole set of additional storyotypes. Dan Rawsthorne, the author, tried to define a storyotype for virtually every possible thing you might need to do in a project. Personally I think he went a bit too far, but a small set of additional storyotypes proved to be very useful anyway. Storyotype Description Compound Epic A composite user story that groups a number of stories in a logical sense. Complex Epic A user story whose content and impact must be determined later in the project, but for which it is clear that it involves a significant amount of work. Setup A story that is used to setup the project environment, including a source control environment, a project website, a build server. Technical A story that involves making a technical improvement or adjustment. Examples include introducing a coding standard, refactoring a poor design, executing a performance test. Documentation A story for writing a user manual, installation manual, etc. Training A story for developing and/or hosting a training, or having a workshop with end users. Quality Improvements A story which objective is to fix a collection of related bugs, or spent a fixed amount of time to improve the quality of the code base. Spike A story that aims to do a technical investigation to determine the usability of a specific technology, or for trying an alternative technical solution. When is the story complete? So how do you know that a user story has been successfully realized? Well, if all is good, all stories will conform with INVEST and are associated with a number of acceptance criteria (typically written down as the how-to demo) specified by the product owner. That should be enough to determine if it is functionally sound. But what you still miss is a way of explaining the stakeholders, including the product owner, when the team treats the story as finished. That may differ by team, but usually includes some or more of the following criteria. The code compiles and there are no warnings or errors. The code meets the coding standards setup by the project or the organization. The code is reviewed by a peer developer. All automated unit and integration tests have completed successfully. Visual Studio’s static code analysis tool does not report any violations. ReSharper reports no potential errors (a.k.a. everything is 'green'). The daily integration build has completed successfully. The functionality was tested by another member of the team (anybody but the developer). The feature or functionality has been signed off using the project checklist. The system functionality is tested by a tester. The visual look and feel is has been approved by an employee of the communications department. Together with the story’s how-to demo these criteria are commonly referred to as the definition-of-done. Usually, a team or project will have a default definition-of-done that applies to all stories and only mentions the particulars of that story if necessary. Then what about the planning? User stories are an excellent unit for tracking progress within your project. However, purists within the Agile community will tell you that an Agile project will have no long term plan. Instead, the functionality is realized iteratively according to the priority defined by the product owner. I agree with the latter and believe that its iterative nature is essential for dealing with the changing requirements that are common in all projects. It allows deferring decisions to the last responsible moment, and that’s always a good thing. But in reality you often can’t escape from providing at least a rough schedule to your management. How should you deal with that? What I often do to get all stakeholders to join me in a number of workshops. Using use case diagrams to illustrate the context of the discussions, I try to get enough stories on paper to represent the entire scope of the project. You need to beware though that you don’t write down too much details or have too much in-depth discussions. That would give the stakeholders a false sense of precision, and consequently, will cause them to see the stories as a formal functional design. Also, if you run into some high-level chunk of functionality for which nobody really knows what it will look like, add an epic story for it and include a spike to elaborate on the epic later on in the project. Then organize a number of shorter meetings with the team or, if the team hasn’t been formed yet, with a few experienced developers. Let them discuss every story one by one and then try to estimate the size of each story in so called story points. Some people from the Agile community say you should estimate using relative sizes only. In other words, a story that seems to require twice as much work as another story should also have twice as many story points. The story point as a unit does not have value. It’s the relative differences that are important. What works for me is that every story point corresponds to the ideal day of an experienced senior software developer. In other words, one story point means that an experienced developer familiar with the chosen architecture, technology and project methodology needs to work for 8 hours without being disturbed by telephone, email, coffee breaks, or any other distractions. Mike Cohn, author of User Stories Applied, has dedicated many chapters to this estimation technique. Ideally, each story is between 1 and 8 story points, but at the beginning of the project you still may have some epics to break up. After finishing those meetings you should have an estimate of the total size of the project. Now, in order to get from those story points to a total number of hours you need to estimate the expected productivity of the team. Mike Cohn does this by creating a table with the expected roles, their availability (to deal with part time employees), and the expected productivity compared to the ideal senior developer (as a percentage). By calculating the average productivity and multiplying it with the number of story points you’ll end up with the total number of estimated man-hours. It’s only an estimate and both the productivity can be disappointing as well as the estimate in story points may appear to be wrong. But it still gives you an initial estimate that can be used for global planning and budget discussions. Obviously it is important to ensure that you keep on continuously measuring the actual productivity. Wow, now what? By now, it should be clear that a user story is not an independent concept but something that closely resonates with many of the aspects of our work in the software industry. In this multi-part post I have tried to explain a number of those aspects and to clarify the relationship between them. But even though I’ve not touched everything as detailed as possible, I still hope I've managed to convince you about the power and potential of user stories. Last but not least, if you have any questions or comments, please do not hesitate to email me at dennis.doomen@avivasolutions.nl or tweet me at my Twitter ID ddoomen.

There are so many differences between Agile and Waterfall that it takes a three-part series to cover it all.