Constraint Test Cases Generation Based on Particle Swarm Optimization

Sheng, Yunlong; Wei, Chang'an; Jiang, Shouda

doi:10.1142/s0218539317500218

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…Sheng et al. [36] proposed two PSO‐based constraint test case generation methods. The influence of parameters of the proposed method on the test suite size is studied in the experimental part, but the problem that PSO is easy to fall into local optimum is not considered, which easily leads to large test suites.…”

Section: Related Workmentioning

confidence: 99%

“…Although experiment results showed that this approach is appropriate for the efficient production of constrained test suites, the method lacks the classification of constraints and details of the corresponding handling strategy, such as implicit constraints. Sheng et al [36] proposed two PSO-based constraint test case generation methods. The influence of parameters of the proposed method on the test suite size is studied in the experimental part, but the problem that PSO is easy to fall into local optimum is not considered, which easily leads to large test suites.…”

Section: Related Workmentioning

confidence: 99%

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A synergic quantum particle swarm optimisation for constrained combinatorial test generation

2022

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Combinatorial testing (CT) can efficiently detect failures caused by interactions of parameters of software under test. The CT study has undergone a transition from traditional CT to constrained CT, which is crucial for real-world systems testing. Under this scenario, constrained covering array generation (CCAG), a vital combinatorial optimisation issue targeted with constructing a test suite of minimal size while properly addressing constraints, remains challenging in CT. To the authors' best knowledge, this paper presents a synergic method first based on quantum particle swarm optimisation (QPSO) for the CCAG problems. Three auxiliary strategies, including contractionexpansion coefficient adaptive change strategy, differential evolution strategy, and discretisation strategy, are proposed to improve the performance of QPSO. Meanwhile, the improved QPSO method combines with the three different constraint handling strategies and an enhanced one-test-at-a-time strategy as a synergic QPSO method named QPIO to solve the CCAG problem. In the experiment, we investigate the impacts of parameter settings on the performance of the QPIO. Extensive experimental results show that the QPIO algorithm is a competitive method compared to the representative methods for CCAG. Besides, the QPIO method enriches the application of the QPSO algorithm in the context of CT.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A synergic quantum particle swarm optimisation for constrained combinatorial test generation

2022

View full text Add to dashboard Cite

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“…Remodel Sub-model [5], [6], [33], [39], [40], [41] Abstract Parameter [6], [40], [42] Avoid Verify [6], [9], [10], [39], [40], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60] Solver [7], [8], [27], [61], [62], [63], [64], [65], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76],…”

Section: Category Technique Referencesmentioning

confidence: 99%

A Survey of Constrained Combinatorial Testing

Wu¹,

Nie²,

Petke³

et al. 2019

Preprint

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Combinatorial Testing (CT) is a potentially powerful testing technique, whereas its failure revealing ability might be dramatically reduced if it fails to handle constraints in an adequate and efficient manner. To ensure the wider applicability of CT in the presence of constrained problem domains, large and diverse efforts have been invested towards the techniques and applications of constrained combinatorial testing. In this paper, we provide a comprehensive survey of representations, influences, and techniques that pertain to constraints in CT, covering 129 papers published between 1987 and 2018. This survey not only categorises the various constraint handling techniques, but also reviews comparatively less well-studied, yet potentially important, constraint identification and maintenance techniques. Since real-world programs are usually constrained, this survey can be of interest to researchers and practitioners who are looking to use and study constrained combinatorial testing techniques.

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“…When the input factors are relative, there are relative constraints between neighboring input time intervals. The construction procedure is different from that of the NCA generation algorithm, as some certain relative constraints must be avoided [20]. This means that the constraint validity of each test case in a CNCA must be checked in the construction process.…”

Section: ) Test Suite Construction Algorithm Of Relative Input Factorsmentioning

confidence: 99%

Constructing Test Suites for Real-Time Embedded Systems Under Input Timing Constraints

2019

Self Cite

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Testing of real-time embedded systems (RTESs) under input timing constraints is a critical issue. Models which can specify timing constraints have respective merits and demerits and test suites which can cover more input possibilities and detect more faults under input timing constraints are worthy of study. In this paper, clocked computation tree logic which is used to specify input timing constraints is presented. Neighbor covering arrays and parallel input time correlation test suites are introduced to test RTESs under serial and parallel input timing constraints. Three algorithms are described in generating test suites, respectively, and corresponding random testing-based algorithms which are used as baselines for comparison are introduced. Benchmarks with different configurations are conducted to evaluate the algorithms' performance. Three real-world RTESs are tested with the test suites described in this paper, respectively. The test results show that random testing may omit some neighbor input time point combinations as the randomness and increase test suite scales. This fact may lead to the omission of some faults and heavy costs. Therefore, the proposed test suites are more effective and efficient for testing RTESs under input timing constraints. INDEX TERMS Real-time embedded systems, input timing constraints, test suite generation, random testing.

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Constraint Test Cases Generation Based on Particle Swarm Optimization

Cited by 11 publications

References 10 publications

A synergic quantum particle swarm optimisation for constrained combinatorial test generation

A synergic quantum particle swarm optimisation for constrained combinatorial test generation

A Survey of Constrained Combinatorial Testing

Constructing Test Suites for Real-Time Embedded Systems Under Input Timing Constraints

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