Bug Lifecycle

Back in the International Conference on Software Engineering (ICSE) 2010, Philip Guo et al published a study on bug resolution in a paper "Characterizing and Predicting Which Bugs Get Fixed: An Empirical Study of Microsoft Windows". The paper investigates the factors that influence bug-fixing decisions in the development of Microsoft Windows, particularly Windows Vista and Windows 7. The authors aimed to understand why some bugs in the Windows operating system are addressed promptly while others remain unresolved for an extended period. They conducted an extensive empirical study by analyzing a large dataset of bug reports and their associated properties from the Windows bug tracking system.

The paper provides several key findings based on their analysis:

1. Bug Characteristics: The study found that certain bug characteristics influence the likelihood of them being fixed. Bugs that are easier to reproduce, have clear descriptions, and affect a wider user base are more likely to be addressed promptly.
2. Bug Severity: The severity of a bug plays a significant role in determining the likelihood of it being fixed quickly. High-severity bugs, which have a substantial impact on user experience or system stability, are given higher priority and are more likely to be fixed sooner.
3. Developer Expertise: The experience and familiarity of developers with specific parts of the codebase influence bug-fixing decisions. Developers tend to fix bugs related to areas they are more knowledgeable about, resulting in variations in bug-fixing rates across different components of the system.
4. Bug Reporting Quality: The quality of bug reports, including the level of detail, reproducibility, and clarity of description, affects the likelihood of a bug being fixed. Reports that provide more precise information are more likely to receive attention and prompt resolutions.
5. Bug Activity and Age: Bugs that receive more comments, indicating active discussions and user interest, tend to be fixed faster. Additionally, the age of a bug influences its chances of being fixed, with older bugs often being prioritized to reduce the backlog.

Based on these findings, the authors developed a prediction model to estimate the probability of bug fixes. They used machine learning techniques to train the model, incorporating various bug characteristics and contextual factors. The model showed promise in predicting which bugs are more likely to be fixed promptly. The paper presented a model based on some definition of bug reporter reputation, frequency of bug edits, bug reassignments, geographical distance, and reopenings. They present both the predictive and descriptive statistical models

How to do a code review, Part 2

Microsoft several years ago published a conversational article on their case study on code review as well as their own in-house code review tool CodeFlow.

1. Code Review Challenges: The article highlights several challenges associated with code reviews, including time constraints, difficulty in coordinating reviewers, lack of context, and limitations of traditional review methods like in-person meetings or email-based reviews.
2. Asynchronous Code Reviews: CodeFlow addresses the challenges by providing a platform for asynchronous code reviews. This allows developers to review and provide feedback on code changes at their own convenience, regardless of time zones or physical location.
3. Distributed Collaboration: CodeFlow enables distributed collaboration by providing a centralized platform where developers can review, comment, and track the progress of code reviews. It eliminates the need for manual coordination and facilitates efficient communication between reviewers and authors.
4. Code Quality Improvement: Code reviews play a crucial role in improving code quality. By involving multiple reviewers, CodeFlow helps identify defects, bugs, and design flaws early in the development process. This leads to better overall code quality and reduces the likelihood of introducing errors into the codebase.
5. Knowledge Sharing: CodeFlow promotes knowledge sharing among developers. Reviewers gain insights into different codebases and programming practices, fostering a culture of learning and continuous improvement. It also helps distribute knowledge across teams and reduces reliance on individual developers.
6. Iterative Review Process: CodeFlow supports an iterative review process where developers can address feedback, make changes, and have subsequent rounds of review. This promotes collaboration, allows for further refinement of code changes, and encourages constructive discussions between authors and reviewers.
7. Metrics and Insights: The article presents statistical data on CodeFlow's usage, including the number of code reviews conducted, average review duration, and the number of code changes accepted or rejected. These metrics provide insights into the effectiveness and efficiency of the code review process.

Check out part 1 of these series on code review.