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
Software developers access bug reports in a project's bug repository to help with a number of different tasks, including understanding how previous changes have been made and understanding multiple aspects of particular defects. A developer's interaction with existing bug reports often requires perusing a substantial amount of text. In this article, we investigate whether it is possible to summarize bug reports automatically so that developers can perform their tasks by consulting shorter summaries instead of entire bug reports. We investigated whether existing conversation-based automated summarizers are applicable to bug reports and found that the quality of generated summaries is similar to summaries produced for e-mail threads and other conversations. We also trained a summarizer on a bug report corpus. This summarizer produces summaries that are statistically better than summaries produced by existing conversation-based generators. To determine if automatically produced bug report summaries can help a developer with their work, we conducted a task-based evaluation that considered the use of summaries for bug report duplicate detection tasks. We found that summaries helped the study participants save time, that there was no evidence that accuracy degraded when summaries were used and that most participants preferred working with summaries to working with original bug reports.
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 | 0.000 | 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 | 0.000 |
| 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 it