A Market-Based Bug Allocation Mechanism Using Predictive Bug Lifetimes
Bibliographic record
Abstract
Bug assignment in large software projects is typically a time-consuming and tedious task, effective assignment requires that bug triagers hold significant contextual information about both the reported bugs and the pool of available developers. In this paper, we propose an auction-based multiagent mechanism for assigning bugs to developers that is intended to minimize backlogs and overall bug lifetime. In this approach, developers and triagers are both modeled as intelligent software agents working on behalf of individuals in a multiagent environment. Upon receiving a bug report, triager agents auction off the bug and collect the requests. Developer agents compute their bids as a function of the developer's profile, preferences, current schedule of assigned bugs, and estimated time-to-fix of the bug. This value is then sent to the triager agent for the final decision. We use the Eclipse and Firefox bug repositories to validate our approach, our studies suggest that the proposed auction-based multiagent mechanism can improve the bug assignment process compared to currently practised methods. In particular, we found a 16% improvement in the number of fixed bugs compared to the historic data, based on a sample size of 213,000 bug reports over a period of 6 years.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".