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
A key collaborative hub for many software development projects is the bug report repository. Although its use can improve the software development process in a number of ways, reports added to the repository need to be triaged. A triager determines if a report is meaningful. Meaningful reports are then organized for integration into the project's development process. To assist triagers with their work, this article presents a machine learning approach to create recommenders that assist with a variety of decisions aimed at streamlining the development process. The recommenders created with this approach are accurate; for instance, recommenders for which developer to assign a report that we have created using this approach have a precision between 70% and 98% over five open source projects. As the configuration of a recommender for a particular project can require substantial effort and be time consuming, we also present an approach to assist the configuration of such recommenders that significantly lowers the cost of putting a recommender in place for a project. We show that recommenders for which developer should fix a bug can be quickly configured with this approach and that the configured recommenders are within 15% precision of hand-tuned developer recommenders.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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