Combinatorial Test Case Generation Using Q-Value Based Particle Swarm Optimization
Bibliographic record
Abstract
Combinatorial testing is an effective method for generating test cases. Pairwise testing is a combinatorial approach that evaluates the interactions between the input test parameters while reducing test case size by selecting a broader search area. Most combinatorial testing research focuses on developing novel approaches for generating an optimal number of test cases that cover pairwise combinations of input test parameters. Using existing test case generation techniques, optimal or near-optimal combinatorial test cases are generated in polynomial time. The authors presented the Q-value-based Particle Swarm Optimization (Q-PSO) technique for efficiently and effectively generating an optimal number of test cases. The primary goals of the proposed technique are to generate test cases using a Q-value based PSO, which is easier to build and has fewer parameters to define than other meta-heuristic search methodologies and to put the proposed technique into practice and report on an empirical study that examines and verifies the significant impact factors in the proposed approach. Q-value is used to evaluate the particles (referred to as test cases) in the Q-PSO. The reward is totalled in the Q-value, which serves as the fitness function for PSO evolution. The Q-value of each particle determines its performance and indicates how quickly the particle can lead the system's state to the set of objective states. The authors used the Q-PSO technique to validate the efficiency and efficacy of the proposed approach. The Q-PSO technique's results are compared to existing metaheuristics and computation-based techniques. In most inputs based on the development environment, meta-heuristic search techniques take significantly longer than other greedy techniques. For some inputs, the proposed Q-PSO technique outperforms existing meta-heuristics techniques. Q-PSO results are also compared to IPOG, ITCH, Jenny, TConfig, TVG, and other well-known computational-based techniques. The goal of the comparison is to examine how the size of the test cases generated has grown over time.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".