Specification-based regression test selection with risk analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it