Works for me! characterizing non-reproducible bug reports
Bibliographic record
Abstract
Bug repository systems have become an integral component of software development activities. Ideally, each bug report should help developers to find and fix a software fault. However, there is a subset of reported bugs that is not (easily) reproducible, on which developers spend considerable amounts of time and effort. We present an empirical analysis of non-reproducible bug reports to characterize their rate, nature, and root causes. We mine one industrial and five open-source bug repositories, resulting in 32K non-reproducible bug reports. We (1) compare properties of non-reproducible reports with their counterparts such as active time and number of authors, (2) investigate their life-cycle patterns, and (3) examine 120 Fixed non-reproducible reports. In addition, we qualitatively classify a set of randomly selected non-reproducible bug reports (1,643) into six common categories. Our results show that, on average, non-reproducible bug reports pertain to 17% of all bug reports, remain active three months longer than their counterparts, can be mainly (45%) classified as "Interbug Dependencies'', and 66% of Fixed non-reproducible reports were indeed reproduced and fixed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".