Best Test Cases Selection Approach Using Genetic Algorithm
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
This paper proposes an approach for selecting best testing scenarios using Genetic Algorithm. Test cases generation approach uses UML sequence diagrams, class diagrams and Object Constraint Language (OCL) as software specifications sources. There are three main concepts: Edges Relation Table (ERT), test scenarios generation and test cases generation used in this work. The ERT is used to detect edges in sequence diagrams, identifies their relationships based on the information available in sequence diagrams and OCL information. ERT is also used to generate the Testing Scenarios Graph (TSG). The test scenarios generation technique concerns the generation of scenarios from the testable model of the sequence diagram. Path coverage technique is proposed to solve the problem of test scenario generation that controls explosion of paths which arise due to loops and concurrencies. Furthermore, GA used to generates test cases that covers most of message paths and most of combined fragments (loop, par, alt, opt and break), in addition to some structural specifications.
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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 | 0.000 |
| 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 | 0.001 | 0.006 |
| 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