Test Generation from an Extended Finite State Machine as a Multiobjective Optimization Problem
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.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".