Software Testing Trends to Win in 2022

Due to the pandemic, the adoption of digital technologies has been phenomenal across the verticals and horizontals.

As per a survey conducted by McKinsey & Company, responses to COVID-19 have only sped up the adoption of digital technologies by several years — and many of these changes could be here for the long haul. 

There has been a culture shift, which has been well supported by these digital technologies. Work from home, door-step service delivery, and digital payments are some of the changes that have easily been made possible with the adoption of these digital technologies.  

Every business understands that the increase in digitization has led to an exponential rise in customer expectations. In the banking sector, customers want to complete their banking needs on mobile in seconds and with no interruption or outages. They need payment applications that are safe and easy to use. Customers also expect the services to be available at any time of the day and accessible from anywhere in the world through the internet. This delivery model is called SaaS (software as a service; or alternatively anything as a service, also known as XaaS). Recently, the SaaS model of delivery has witnessed an exponential rise in demand for application delivery.

To meet business demand, IT organizations are looking to build fast, intelligent, scalable, and connected applications to serve their customers better. The need has brought a significant impact on the software testing landscape. Let us inspect the drivers in IT organizations that are contributing to the changing landscape. 

Drivers Leading the Change in Software Testing in 2022

Building a Faster Application With Agile and DevOps Culture

Most IT organizations are adopting Agile and DevOps practices for their software delivery because they want to deploy and deliver better software faster.  

The Agile approach involves a continuous process of requirement discovery and solution improvement through collaboration among self-organizing cross-functional teams. Most of the quality assurance practices involve an agile way of delivery. DevOps methodology breaks siloes between software development, and operations help to deliver software faster, safer, and at scale.

Traditionally, testing teams would be working on a build that is deployed in a development environment and would manually perform regression testing. However, with the new culture delivering applications to the market faster, the demand for testing applications to find risks and bugs in a shorter duration and with limited cost will only rise.

Building an Intelligent Application With AI/ML

AI/ML technologies are being leveraged to provide advanced customer experience through curated recommendations, ease of resolving grievances, and much more. Natural language processing (NLP) is another arm of AI/ML that has transcended experience by understanding human demands through a spoken language. B2C and B2B organizations across domains have adopted and embedded AI/ML into their applications and they are accelerating their innovation to serve their customers in a better way.

As the usage of AI/ML software increases, organizations are supposed to focus more on the testing of these apps using some automation tools. 

Building Scalable Applications With Containerization and Microservices

All organizations are architecting distributed systems using microservice architecture and containers; the primary reason is to scale as per business demand. Containerized apps are a favorite to DevOps and site reliability engineering (SREs) because they provide a scalable computing environment quickly and reliably run applications as a form of the package (code along with all the dependencies, also called artifacts). Other benefits include containers requiring fewer resources, and containerized apps can be rapidly deployed, patched, or scaled into any cloud.

Containerized apps and microservices are good, but they bring their own set of challenges, such as being prone to attack. Since services are loosely coupled and talk to each other or are dependent, failure of one service means failure of the whole application. 

There is a need for a proper strategy to test the complete containerized application and verify all the interactions between these microservices using sanity testing and smoke testing before releasing a new feature to production.

Building Connected Applications With APIs

API is the middle layer between the UI and back-end (presentation layer and data layer). APIs connect distributed systems and transfer data from one system to another. With the increased adoption of microservices, usage of APIs has also increased exponentially.

For example, a retail website can have many small services such as catalog, product search, order submissions, inventory recommendations, etc., that are connected using APIs. While APIs hold the key for digital transformation, they also open the door for potential security threats.  

To make an application free from all vulnerabilities, testing APIs is necessary. API testing needs to do more than just validate the optimal implementation of APIs. The QA team should also assess APIs for security risks and threats and help businesses offer a secure and reliable platform to their customers and partners. 

Building Secured Applications

There is a growing demand for application security with the advent of digitization and increased adoption of technology. The world has become more mobile, and business groups are investing in their users to interact with their services through an app on various devices, such as Microsoft Teams, Zoom, WebEx, and so on. 

Any loopholes pose a threat to businesses. Weak server-side controls, unsafe data storage, broken cryptography, and other problems open the door for external attackers to steal confidential and sensitive data. The cases of the SolarWinds Orion supply chain attack and Log4j2 bugs are so fresh, and they remind us of what could be the potential loss of security threats.

Now that we have read about the drivers, let us see how the software testing landscape is shaping up in 2022.

Top 8 Software Testing Trends in 2022

Software testing is a critical piece of the puzzle in creating a fast, intelligent, scalable, connected, and secured application. After all, it just takes a few seconds for customers to form an opinion about a brand after they browse through an application or website.

However, legacy processes or technologies in software testing will be a hindrance for IT organizations to scale. CIOs, enterprise architects, and QA managers need to take preemptive action and adopt a new strategy in quality assurance to meet the growing business demands.  

Below are eight trends that business executives, CIOs, and platform architects should look at, with the last six including model-based and architecture-based software testing:

1. Autonomous testing will be at the heart of adoption.
2. The convergence of different tools for test design, test execution and test planning
3. Architecture-based software testing to increase test coverage
4. Shift-left testing
5. More stress on security testing to mitigate risks
6. API testing for checking APIs and integrations
7. The rise of mobile application testing 
8. Testing of AI/ML models in application

Trend 1: Autonomous Testing Will Be at the Heart of Adoption 

Applications are rolled out faster to serve the end customers and testing is expected to happen on time. However, for QA engineers, although they use automated testing tools, they still rely on generating automated test scenarios. Hence, organizations need to generate test cases faster, execute them, and finally analyze test results without human intervention. This process is called autonomous testing, and every organization will require this.

Autonomous testing ensures software testing platforms have a unified, 360-degree view of the test application. Autonomous testing platforms use AI and ML to be contextually aware and automatically detect defects within the test application. They can further execute test scenarios with minimal to no human intervention. 

Autonomous testing has gained interest among the testing community and will grow in the coming years. As per the Omdia Report 2021 on autonomous testing, most organizations will adopt autonomous testing in 2022 (refer to the figure below). And almost 90% of all surveyed companies will have fully deployed autonomous testing by 2024.  

Trend 2: Convergence of Different Tools for Test Design, Test Execution, and Test Planning

With more focus to accelerate testing for the many applications rolling out every day, a tester would have to think of ways to optimize the software testing process and reduce the time.

The test management process involves managing the testing activities end-to-end. This process involves test planning, test design, and test execution. When managed and coordinated effectively, these activities discipline the software testing process.

• Test planning stage: Consists of a document that involves a detailed report about the scope and approach for testing activities. 
• Test design stage: Consists of creating the suitable test suite (test scenarios and test cases) for testing the application. 
• Test execution stage: Test execution involves execution on time and within budget, and monitoring results to verify all the metrics.

Most of the testing tools cater to the execution and planning stages of test management. Multiple tools are being used to provide test design, execution, or planning. This will concern the QA and finance teams regarding resources to manage those tools and costs. Hence, we feel there will be a convergence across tools for various stages in software testing.

Trend 3: Architecture-Based Software Testing to Increase Test Coverage

Test scenario creation can be done in multiple ways by QA engineers. But one of the most optimized ways of generating tests is model-driven architecture based testing (MDABT). MDABT considers the architecture of the application under test and creates the test suite and application blueprint. Model-based testing (MBT) automates the generation and execution of test scenarios and test automation scripts using models-based techniques on application requirements and behavior (refer to the image below). A model is generated for the application under test. Based on this model, abstract tests are generated, and an executable test script is created which helps in testing the application.

 The significant benefits of MBT include: 

• Improved test coverage
• Reduced testing time and increased reliability
• Reusability of tests
• Less human effort
• Reduction in cost
• Improved fault detection
• Improved product quality

Trend 4: Shift-Left Testing

To improve the confidence in a software release, testing has to be carried out from the very beginning — also known as shift-left testing.

Shift-left testing is an approach to software testing and system testing in which testing is pushed towards the early stages of software development (refer to the image below). Organizations can detect issues early in the software development lifecycle. The developers and testers have a similar day in this kind of testing, where software development and software testing move parallelly.

The shift-left testing approach helps enterprises achieve a quality-first mindset. This testing mechanism helps them track performance metrics right from the inception of software development.

The shift-left approach, along with DevOps and automated tests, has unlocked the possibilities of continuous testing while ensuring a smaller turnaround time, efficient detection of defects at earlier stages of development, lesser manual intervention, and shorter execution and release cycles.

The software testers need to not be restricted or limited to testing alone.  Developers too can test in parallel to development, and testers can develop and test in parallel.

As per David Moss, 

"A leading software testing consultant, shift-left testing can be done even before there’s a code. At the beginning of application development, there is no software application. A quality assurance function can be performed by participating in requirement and design reviews, in hope of driving defects earlier in the process.”

Trend 5: More Stress on Security Testing to Mitigate Risks

Building an intelligent and scalable system using a multi-cloud approach is not enough if the application is vulnerable to security risks and cyber-attacks. Rampant security attacks make headlines and also remind us that just rolling out applications faster to the cloud is not enough.

Every application must go through rigorous security testing to free it from potential risks, threats, and vulnerabilities, thereby reducing any possibility for loss of information, revenue, or reputation. Organizations have to use methodologies such as static application security testing (SAST) to examine code to find software flaws and weaknesses such as SQL injection. They also use dynamic application security testing (DAST) to examine an application as it’s running to find vulnerabilities that an attacker could exploit.

Certain tools that help in SAST and DAST scanning are HP Appscanner, JFrog Xray, and SonarQube. 

According to Gartner, with the increased adoption of digitization and multi-cloud, IT investment in security testing will grow at a CAGR of 22.3% by 2025.

Trend 6: API Testing for Checking APIs and Integrations

API testing is a type of software testing that involves testing application programming interfaces directly and as part of integration testing to determine if it meets the required criteria for functionality, reliability, performance, and security.

The rapid adoption of cloud computing has highlighted the importance of application programming interfaces (APIs). With the rise in cloud applications, microservice architecture, and connected platforms, API testing is a necessity. 

Data Bridge Market Research estimates a splurge in "the adoption of API technology by both small and mid-sized enterprises and large enterprises," stating that "rising complexities in the information technology sector and increased focus on the implementation of advanced technologies for software development will likely drive the growth of the API testing market."

Trend 7: The Rise of Mobile Application Testing 

With the need to serve customers using apps, the apps on various devices such as laptops and smartphones also come up. And as more and more users spend an increasing amount of time on their mobile devices, it becomes essential for B2C and B2B enterprises to deliver a quality experience for mobile applications. If these applications fail to work or do not provide the desired user satisfaction, it will result in irate customers and negative feedback on different social media channels. Ensuring that these applications provide the best mobile experience through continuous mobile testing will be crucial for companies offering smartphone apps to deliver services. 

Mobile testing is a process by which application software developed for mobile devices is tested for its functionality, usability, load, and consistency.  

According to a comprehensive research report on mobile testing by Market Research Future (MRFR), the number of enterprises adopting mobile application testing is supposed to grow at a CAGR of 20.3%.

Trend 8: Testing of AI/ML Models in Application: 

Usage of AI/ML will continue to grow in software development. Many arms of AI such as machine learning (ML), natural language processing (NLP), natural language understanding (NLU), optical character recognition (OCR), deep learning, and so on have been used profusely.

The primary purpose of testing AI models is to determine the accuracy. Testing with AIML involves testing the models with a massive set of data divided into training and testing sets. Testing these models becomes challenging as test cases depend on the AI model and the creation of massive sets of data. Testing all these models might require testers to understand the AI technology, or at least the basics. (Testers usually refer to them as black box AI models.) 

Based on the discussion with QA analysts and engineering heads of many Fortune 500 companies, I have concluded that although the AI and ML testing is picking up, it is still being carried out by developers or the analytics team themselves. Once the momentum picks up in AI and ML development in the next three to four years, testers will perhaps be involved in testing these technologies with the help of new tools that can generate massive data sets.