Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation
Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in part...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
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The Institution of Engineering and Technology
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/24035/1/fuzzy%20adaptive%20tuning.pdf |
| _version_ | 1825812540561555456 |
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| author | Kamal Z., Zamli Ahmed, Bestoun S. Mahmoud, Thair Afzal, Wasif |
| author_facet | Kamal Z., Zamli Ahmed, Bestoun S. Mahmoud, Thair Afzal, Wasif |
| author_sort | Kamal Z., Zamli |
| collection | UMP |
| description | Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem \cite{BestounKamalFuzzy2017}. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system \cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work. |
| first_indexed | 2024-03-06T12:30:42Z |
| format | Book Chapter |
| id | UMPir24035 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:30:42Z |
| publishDate | 2018 |
| publisher | The Institution of Engineering and Technology |
| record_format | dspace |
| spelling | UMPir240352019-01-29T03:52:56Z http://umpir.ump.edu.my/id/eprint/24035/ Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation Kamal Z., Zamli Ahmed, Bestoun S. Mahmoud, Thair Afzal, Wasif QA Mathematics Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem \cite{BestounKamalFuzzy2017}. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system \cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work. The Institution of Engineering and Technology 2018 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24035/1/fuzzy%20adaptive%20tuning.pdf Kamal Z., Zamli and Ahmed, Bestoun S. and Mahmoud, Thair and Afzal, Wasif (2018) Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation. In: warm Intelligence Volume 3: Applications. The Institution of Engineering and Technology, London, pp. 1-21. ISBN 978-1-78561-631-0 https://arxiv.org/abs/1810.05824 |
| spellingShingle | QA Mathematics Kamal Z., Zamli Ahmed, Bestoun S. Mahmoud, Thair Afzal, Wasif Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation |
| title | Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation |
| title_full | Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation |
| title_fullStr | Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation |
| title_full_unstemmed | Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation |
| title_short | Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation |
| title_sort | fuzzy adaptive tuning of a particle swarm optimization algorithm for variable strength combinatorial test suite generation |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/24035/1/fuzzy%20adaptive%20tuning.pdf |
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