Investigating the information value of different sources of evidence of developers’ expertise for bug assignment in open‐source projects
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
Bug assignment (BA), the process of ranking developers according to their potential ability to fix a given bug, is an important software‐engineering task. BA usually requires the development of an expertise profile for each developer, and formulation of a similarity metric to estimate the relevance of developers to the bug. This needs us to answer the following question: ‘what is the information value of various contributions of developers in BA research?’ We address this question by making the following contributions. (i) We enhance the expertise metric of our prior work, vocabulary and time‐based BA, to consider information regarding various sources of expertise with different importance. We show that this can improve the effectiveness of bug‐assignment process. (ii) Using this ‘Multisource’ expertise metric, we investigate the information value of different pieces of information in open‐source repositories for BA. We show that in addition to bug‐fixing contributions, other technical and even social contributions of developers within the version‐control system are useful information for BA. (iii) We provide a curated, up‐to‐date data set including technical information of 13 popular open‐source projects in Github. To the best of our knowledge, this is the most comprehensive data set, currently available for bug‐assignment research.
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 | 0.001 | 0.007 |
| 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 | 0.002 | 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