High-impact defects
Bibliographic record
Abstract
The relationship between various software-related phenomena (e.g., code complexity) and post-release software defects has been thoroughly examined. However, to date these predictions have a limited adoption in practice. The most commonly cited reason is that the prediction identifies too much code to review without distinguishing the impact of these defects. Our aim is to address this drawback by focusing on high-impact defects for customers and practitioners. Customers are highly impacted by defects that break pre-existing functionality (breakage defects), whereas practitioners are caught off-guard by defects in files that had relatively few pre-release changes (surprise defects). The large commercial software system that we study already had an established concept of breakages as the highest-impact defects, however, the concept of surprises is novel and not as well established. We find that surprise defects are related to incomplete requirements and that the common assumption that a fix is caused by a previous change does not hold in this project. We then fit prediction models that are effective at identifying files containing breakages and surprises. The number of pre-release defects and file size are good indicators of breakages, whereas the number of co-changed files and the amount of time between the latest pre-release change and the release date are good indicators of surprises. Although our prediction models are effective at identifying files that have breakages and surprises, we learn that the prediction should also identify the nature or type of defects, with each type being specific enough to be easily identified and repaired.
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.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".