A case study in the use of defect classification in inspections
Bibliographic record
Abstract
In many software organizations, defects are classified very simply, using categories such as Minor, Major, Severe, Critical. Simple classifications of this kind are typically used to assign priorities in repairing defects. Deeper understanding of the effectiveness of software development methodologies and techniques requires more detailed classification of defects. A variety of classifications has been proposed.Although most detailied schemes have been developed for the purpose of analyzing software processes, defect classification schemes have the potential for more specific uses. These uses require the classification scheme to be tailored to provide relevant details. In this vein, a new scheme was developed to evaluate and compare the effectiveness of software inspection techniques. This paper describes this scheme and its use as a metric in two empirical studies. Its use was considered successful, but issues of validity and repeatability are discussed.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".