Impact and Performance of Randomized Test-Generation using Prolog
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911061996732416 |
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| author | Gelderie, Marcus Luff, Maximilian Peltzer, Maximilian |
| author_facet | Gelderie, Marcus Luff, Maximilian Peltzer, Maximilian |
| contents | We study randomized generation of sequences of test-inputs to a system using Prolog. Prolog is a natural fit to generate test-sequences that have complex logical inter-dependent structure. To counter the problems posed by a large (or infinite) set of possible tests, randomization is a natural choice. We study the impact that randomization in conjunction with SLD resolution have on the test performance. To this end, this paper proposes two strategies to add randomization to a test-generating program. One strategy works on top of standard Prolog semantics, whereas the other alters the SLD selection function. We analyze the mean time to reach a test-case, and the mean number of generated test-cases in the framework of Markov chains. Finally, we provide an additional empirical evaluation and comparison between both approaches. Under consideration in Theory and Practice of Logic Programming (TPLP). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_13178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact and Performance of Randomized Test-Generation using Prolog Gelderie, Marcus Luff, Maximilian Peltzer, Maximilian Logic in Computer Science We study randomized generation of sequences of test-inputs to a system using Prolog. Prolog is a natural fit to generate test-sequences that have complex logical inter-dependent structure. To counter the problems posed by a large (or infinite) set of possible tests, randomization is a natural choice. We study the impact that randomization in conjunction with SLD resolution have on the test performance. To this end, this paper proposes two strategies to add randomization to a test-generating program. One strategy works on top of standard Prolog semantics, whereas the other alters the SLD selection function. We analyze the mean time to reach a test-case, and the mean number of generated test-cases in the framework of Markov chains. Finally, we provide an additional empirical evaluation and comparison between both approaches. Under consideration in Theory and Practice of Logic Programming (TPLP). |
| title | Impact and Performance of Randomized Test-Generation using Prolog |
| topic | Logic in Computer Science |
| url | https://arxiv.org/abs/2507.13178 |