Regression test suite selection using dependence analysis
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Bibliographic record
Abstract
SUMMARY Dependence analysis on an Extended Finite State Machine representation of the requirements of a system under test identifies various types of control and data dependencies between transitions caused by a set of modifications on the requirements. These particular types of dependencies capture the effects of the modifications, that is, their direct effects on the changed parts of the system and their side effects on the unchanged parts of the system. Recent work on model‐based regression testing shows that dependencies capturing direct effects and side effects of the changes made on the requirements can be used for regression test suite (RTS) reduction (reducing the size of a given test suite by eliminating redundancies), for RTS prioritization (ordering test cases in a given test suite for early fault detection), or for RTS generation (designing a test suite covering the identified dependencies). This paper proposes an additional use of such dependencies, namely, RTS selection , which is the process of selecting a subset of a given test suite to form an RTS by considering the coverage of dependencies related to the effects of the modifications. The dependencies marked during this process as uncovered provide a basis for augmenting an (incomplete) RTS with test cases covering uncovered dependencies. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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