An Empirical Study on Factors Impacting Bug Fixing Time
Bibliographic record
Abstract
Fixing bugs is an important activity of the software development process. A typical process of bug fixing consists of the following steps: 1) a user files a bug report, 2) the bug is assigned to a developer, 3) the developer fixes the bug, 4) changed code is reviewed and verified, and 5) the bug is resolved. Many studies have investigated the process of bug fixing. However, to the best of our knowledge, none has explicitly analyzed the interval between bug assignment and the time when bug fixing starts. After a bug assignment, some developers will immediately start fixing the bug while others will start bug fixing after a long period. We are blind on developer's delays when fixing bugs. This paper explores such delays of developers through an empirical study on three open source software systems. We examine factors affecting bug fixing time along three dimensions: bug reports, source code involved in the fix, and code changes that are required to fix the bug. We further compare different factors by descriptive logistic regression models. Our results can help development teams better understand factors behind delays, and then improve bug fixing process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".