Modelling the ‘Hurried’ bug report reading process to summarize bug reports
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although bug reports are frequently consulted project assets, they are communication logs, by-products of bug resolution, and not artifacts created with the intent of being easy to follow. To facilitate bug report digestion, we propose a new, unsupervised, bug report summarization approach that estimates the attention a user would hypothetically give to different sentences in a bug report, when pressed with time. We pose three hypotheses on what makes a sentence relevant: discussing frequently discussed topics, being evaluated or assessed by other sentences, and keeping focused on the bug report's title and description. Our results suggest that our hypotheses are valid, since the summaries have as much as 12% improvement in standard summarization evaluation metrics compared to the previous approach. Our evaluation also asks developers to assess the quality and usefulness of the summaries created for bug reports they have worked on. Feedback from developers not only show the summaries are useful, but also point out important requirements for this, and any bug summarization approach, and indicates directions for future work.
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.
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 | 0.003 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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 it