Leveraging the Power of Images: Image Recommendation to Enhance Issue Reports
Bibliographic record
Abstract
ABSTRACT Background The trend of sharing images and image‐based social networks has eventually changed the landscape of social networks. Objective This study focuses on three primary objectives: (i) identifying issue reports that benefit from image sharing and processing in Bugzilla, (ii) identifying the type of image that would improve the bug report, and (iii) conducting a comprehensive qualitative and quantitative evaluation of the tool's performance and impact. Methods We trained machine‐learning and deep‐learning models on a dataset of 34,540 Bugzilla issue reports. The results are evaluated using quantitative performance metrics and a qualitative survey. Results Our prediction method achieves an F1‐score of 0.77 and about 75% of participants found it practically useful in our study. Conclusion This study and its associated dataset and methodology represent the first research on recommending images to developers for enhanced issue report communication. Our results illuminate a promising trajectory for enhanced and visual productivity tools for developers.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".