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