Effective Discrete Memetic Algorithms for Covering Array Generation

Guo, Xu; Song, Xiaoyu; Zhou, Jian

doi:10.1109/compsac.2018.00047

Cited by 4 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It started at an initial temperature T i , which will be decremented at each iteration by using the relation T k+1 = σT k . In this study, we fix σ = 0.78 and final temperature T min = 1.0 � 10 −6 based on our preliminary experiment and work [53] and adjust the initial temperature T i to achieve the best performance, where the tuning process and final setting value of T i are shown in Section 5. QSSMA adopts One-test-at-a-time Strategy as the CA generation strategy, and the detailed process is shown in Algorithm 4.…”

Section: Sa-based Local Searchmentioning

confidence: 99%

“…In this section, we designed an experiment to research the influence of different parameter settings on the test suite's size step by step. According to the previous experimental experience, we know that different parameter configurations of the algorithm will more or less impact the performance of CA generation methods [29,53]. In the experiment, we gradually explore the optimal parameter configuration for the QSSMA algorithm and give the process of finding the optimal parameter allocation through two benchmark instances.…”

Section: Parameter Tuningmentioning

confidence: 99%

See 1 more Smart Citation

A memetic algorithm for high‐strength covering array generation

et al. 2023

Self Cite

View full text Add to dashboard Cite

Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP-complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t, the techniques for constructing highstrength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high-strength CAG problem. The sub-optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high-covering strength test suites. The QSSMA method could successfully build high-strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state-of-theart methods. K E Y W O R D S optimisation, particle swarm optimisation, software engineering | INTRODUCTIONSoftware testing is an important area of software engineering and has great significance in ensuring software quality. Due to the large amount of test data required by modern software, if a complete test is performed, the size of the test data will increase sharply as increasing parameters or parameter values, leading to a lower testing efficiency. Thus, effective failure detection at a low cost is a critical issue for test case generation. Combinatorial testing (CT), a popular testing method, can detect failures triggered by various interactions of factors [1].There are two key issues in the field of combinatorial testing: (1) how to construct a test suite as small as possible; (2) how to find the combination of parameters that trigger the fault in a particular test case that triggers a software under test (SUT) failure. Researchers are more concerned with problem one, known as covering array generation (CAG) [2]. The main idea of CT is to construct a test suite represented by a covering array (CA) that contains all combinations of parameter's value of the SUT at least once and hopes the array is as small as possible. The generation of the smallest size test suite has been proven to be an NP-complete problem [3]. Numerous methods have been proposed, including mathematical, greedy, and artificial intelligence-based search methods. However, all the proposed mathematical methods apply only to restrictive parameter sets. Besides, greedy algorithms often result in oversized test suites. Due to this limitation, scholars focus more on AI-based (heuristic) search methods [4].Many efficient heuristic search algorithms have been successfully applied to CA generation, including geneticThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Sa-based Local Searchmentioning

confidence: 99%

Section: Parameter Tuningmentioning

confidence: 99%

A memetic algorithm for high‐strength covering array generation

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, due to the particle position is updated according to Equations ( 19) and ( 22), the value of a particle's position component may be beyond the reasonable range of the corresponding factor, namely, x i,j ∉ D j . A method proposed in our previous research [45] named superiority wall is adopted to solve this problem. The superiority wall is inspired by the absorption wall and reflection wall proposed by Robinson [46] but has better performance in solving the system constraints.…”

Section: System Constraint Handling Strategymentioning

confidence: 99%

A synergic quantum particle swarm optimisation for constrained combinatorial test generation

2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract