Automatically predicting bug severity early in the development process
Bibliographic record
Abstract
Bug severity is an important factor in prioritizing which bugs to fix first. The process of triaging bug reports and assigning a severity requires developer expertise and knowledge of the underlying software. Methods to automate the assignment of bug severity have been developed to reduce the developer cost, however, many of these methods require 70-90% of the project's bug reports as training data and delay their use until later in the development process. Not being able to automatically predict a bug report's severity early in a project can greatly reduce the benefits of automation. We have developed a new bug report severity prediction method that leverages how bug reports are written rather than what the bug reports contain. Our method allows for the prediction of bug severity at the beginning of the project by using an organization's historical data, in the form of bug reports from past projects, to train the prediction classifier. In validating our approach, we conducted over 1000 experiments on a dataset of five NASA robotic mission software projects. Our results demonstrate that our method was not only able to predict the severity of bugs earlier in development, but it was also able to outperform an existing keyword-based classifier for a majority of the NASA projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".