Test Generation from an Extended Finite State Machine as a Multiobjective Optimization Problem
Why this work is in the frame
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Bibliographic record
Abstract
Extended Finite State Machines are widely used in different phases of software development including software testing. In this Ph.D. dissertation, we argue that test generation from an Extended Finite State Machine (EFSM) can be considered as a multiobjective optimization problem. When a test engineer generates tests from an EFSM he/she typically considers several objectives. We propose a search-based approach to generate test suites from an EFSM, accounting for multiple (potentially conflicting) such objectives. We aim at maximizing coverage of the EFSM test model and maximizing feasibility of the generated test suite so that its test cases can actually execute, while minimizing similarity between these test cases since this has been shown to increase fault detection, as well as minimizing overall cost. Therefore, we have defined a multiobjective genetic algorithm that searches for optimal test suites based on four fitness functions. In doing so, we create an entire test suite at once as opposed to creating a test suite one test case one at a time, which we argue is a suboptimal test suite generation procedure. Our approach is evaluated on different case studies, showing interesting results. We also investigate different ways of improving our solution and analyze impact of those improvements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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