Identifying and Detecting Inaccurate Stack Traces in Bug Reports
Bibliographic record
Abstract
Bug reports contain a combination of structured and unstructured information, with the stack trace being one of the most important structured pieces of information. However, stack traces that do not contain accurate fault location information can lead to significant delays in software maintenance. This work examines eight projects from two open-source software communities to understand how to identify and detect inaccurate stack traces in bug reports. Using our approach to detecting inaccurate stack traces, we found that accurate fault location information is found roughly $33 \%$ of the time in the top frame, ${5 0 \%}$ in the Top-3 frames, ${7 5 \%}$ in the Top-5 frames and that the information accuracy does not significantly improve beyond this point. Next, we found that roughly $33 \%$ of stack traces can be considered inaccurate using the Top-5 stack trace frames. Third, NPEs commonly occur in inaccurate stack traces, and project-specific exceptions occur too infrequently to likely be useful in identifying inaccurate stack traces. Finally, the best machine learning classifier to identify inaccurate stack traces in a project’s bug reports uses a linear regression algorithm trained using the filename, method name and exception name from the top frame of a stack trace. Depending on the project, this classifier has a ${7 8 - 9 8 \%}$ accuracy, ${6 0 - 9 4 \%}$ precision, and $16-97 \%$ recall.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".