From Code to Insight: Using NLP and Sentiment Analysis in Git History

Technological development widens the capabilities of project management tools, enabling them to cover the areas they had long been unable to cover. Natural Language Processing (NLP) has made it possible to conduct a deep analysis of human communication in project development teams to understand bottlenecks and advantages in order to find optimal solutions. In this article, I will explain how to utilize technical textual data like commit messages and documentation to transform traditional team management and achieve better results.

Project Management

The core goal of project management is to arrange processes and manage human teams to ensure successful project delivery. Dealing with teams is one of the toughest parts of this complex process, which includes recruiting people with relevant skills, training and motivating them, as well as evaluating their performance. Until recently, traditional project management lacked viable tools for analyzing subtle yet crucial aspects that affect both personal and team performance—context, emotions, and sentiments. These components help understand how team members feel about the project and properly address their concerns to improve morale and efficiency.

How to Understand What Team Members Really Think?

All human communication can help get a clear vision of team member sentiments: commits, documentation, messages, feedback, and conversations. One of the places that accumulates all this information is the git log. Commit messages in the git log, for example, are a very valuable source of diverse data, such as meta-information about the code, explanations, and various links to ticketing systems. Analyzing the content and communication style in these messages gives valuable insights into the emotional state of employees, which affects team dynamics and the overall project progress. And NLP techniques give an opportunity to analyze vast volumes of commit messages.

What Perspectives Do Different Team Members Have?

The git log can become an effective tool for improving teamwork, but only if you clearly understand the roles and responsibilities of different team members. Let’s have a brief look at how various employees use the git log.

Developer

Developers use the git log as a “time machine” to understand the history of the codebase. They read commit messages to trace back the development of various features, find explanations for past decisions, and find solutions for fixing bugs. The git log can help grasp the true scale of the contributions of project developers and identify the project areas that need additional work or resources.

Team Lead

By leveraging the git log, team leads can understand the human aspect of the project development process. By going through the commit messages, they can see if every team member is aligned with the project goals. Sentiments in commit messages can help leads assess team morale and fix problems affecting productivity.

Analyst

The git log is a source of useful information about the feature implementation logic for analysts. They can use it to trace back all the technical decisions, detect what aspects of the development process caused difficulties, and focus on fixing problematic parts of the code or workflow.

Project Manager

The git log is a tool project managers use to track the process of project milestone realization. By analyzing team communication, they can clearly see if the project is developing as planned. This can help them adjust timelines and divert additional resources to the problematic areas.

Product Manager

Despite the fact that the product managers don’t usually interact with the git log, they can still use it. The git log can help product managers understand what the team thinks about various product features. As the team members are often the first product users, their opinions can be valuable for understanding what features are deemed useful and devising a more precise product strategy.

CTO/CIO

CTOs and CIOs are not frequent git log users because they are always preoccupied with the broader project vision. Still, they can use git log insights to paint a full picture of team dynamics and project health. This information can be handy for adjusting strategic plans in accordance with the state of the team.

HR

HR departments turn to the git log only when recruiting new employees. However, they can use sentiment analysis to detect problems negatively affecting the work environment and address them to boost productivity.

Top Management

Top managers rely on their CTOs and CIOs for updates on the project. However, the sentiment analysis can be a supplementary source of data, which can show if the project remains on track.

How Does Natural Language Processing (NLP) Help in Git Log Analysis?

Natural Language Processing (NLP) is the key to automating and improving the quality of textual data analysis. Thanks to its capacity to examine the linguistic features of text, NLP can detect emotional tone and find common patterns in the commit messages. This enables a deep analysis of large amounts of commit messages stored in the git log, which is essential for a full understanding of the project development process.

A typical NLP pipeline for processing textual data contains the following stages:

Preprocessing

At first, the data is cleaned, with “stop words” (common words that do not carry significant meaning, such as “the,” “is,” and “and”) being removed. At the same time, the context and metadata get added to enrich the initial data.

Tokenization

Textual data is split into individual words or tokens, which are fundamental units in the NLP analysis.

Vectorization

Next, the text data is converted into numerical vectors that machine learning algorithms can process. This allows for computational analysis of textual information.

Machine Learning Processing

This is a complex process, which starts with identifying the emotional tone of messages (positive, negative, or neutral) and detecting patterns in the text (recurring themes or common phrases). After that, the text is put in predefined categories or tagged with relevant labels. Finally, deep learning algorithms are used in ML model training to improve the analysis quality. Processing more data, deep learning models learn to make increasingly accurate predictions over time.

Building an effective NLP pipeline for dealing with commit messages in the git log requires an extended toolkit for handling various operations like pattern recognition, and machine learning. I recommend the following options:

• TextBlob 
• NLTK (Natural Language Toolkit) 
• SpaCy 
• scikit-learn (sklearn) 
• Gensim 
• TensorFlow and Keras 

During training, it’s important to remember that NLP model tuning is an iterative process. That is why you need constant adjustments and refinements to tackle ever-present challenges and maintain high levels of accuracy. 

Small commit messages are a common problem because they usually lack sufficient information that indicates specific sentiments. To overcome this challenge, validating the sentiment scores and categories against the original commits to ensure they make sense in the given context is needed.

Other points of consideration include slang words and the writing style of developers. For example, developers may use common neutral terms such as "null," "error," and "bug," in negative connotations. This can lead to unrealistically low sentiment scores in commit messages, which is a problem developers have to be aware of. 

NLP Insights: PM/Lead Toolset 2.0

NLP techniques equip project managers and team leads with highly effective tools that can increase team productivity by:

• Tracking team morale and identifying possible human-related problems. 
• Detecting the problematic parts of the codebase and tracking the process of their improvement, which may include bug fixes, refactoring, and the addition of new features.
• Tagging commits with relevant labels like ‘bug fix’ for an automatic search in the commit history and easier navigation.
• Finding vague messages and using them to create clear communication rules for the entire team.
• Identifying rogue commits, potential sabotage activities, or unintended bulk changes to uphold high codebase quality.
• Using commit message analysis as a basis for sprint planning.
• Identifying areas of expertise within the team for better task assignments.
• Determining personal work ethics and attitude evolution of employees for increasing development efficiency.