Specification-based regression test selection with risk analysis
Bibliographic record
Abstract
Regression testing is essential to ensure software quality. The test team applies a regression test suite to ensure that new or modified features do not regress (make worse) existing features. Although existing research has addressed many problems and put forward solutions, most regression test techniques are code-based. Code-based regression test selection is good for unit testing, but it has a scalability problem. When the size of the subject under test grows, it becomes hard to manage all the information and to create corresponding traceability matrices. In this paper, we describe a specification-based method for regression test selection.The basic model we use for describing requirements based on customer features or behaviors is the activity diagram, which is a notation of the Unified Modeling Language (UML). A process for identifying the affected test cases is presented. To summarize our approach, we select two kinds of regression tests: i) Targeted Tests, which ensure that important current customer features are still supported adequately in the new release and ii) Safety Tests, which are risk-directed, and ensure that potential problem areas are properly handled. Our test selection technique will be based on a practical risk analysis model.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".