Understanding and predicting bugs fixed by API-migrations
Bibliographic record
Abstract
Bug tracking systems are standard repositories that preserve a large number of uncovered bugs. Once a bug is reported in these repositories, developers search for appropriate changes to fix the bug. However, discovering the changes that can fix the bugs has a negative influence on the schedule and cost of projects. Mainly, fixing bugs could be done by performing some other maintenance changes. In this work, we study and examine the role of adaptive maintenance in the context of API-migration during bug fixing activities through a case study on KOffice, Extragear/graphics, and Open Scene Graph projects. Our goal is to direct developers towards potential bugs early in development which are more likely to be fixed by performing adaptive changes as opposed to other maintenance tasks. We examined the reports of fixed bugs from the bug tracking systems of the studied projects, then we explored several factors related to variant dimensions of the reports and their relevant version history commits, in order to evaluate their effectiveness to decide whether a bug is likely to be fixed by adaptive changes. Our case study results show that bug residency time, textual contents of the report, the component that the bug was found in, and reporter/commenter experience show significant differences between the bugs that are fixed by adaptive changes and other fixed bugs.
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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 | 0.007 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".