Improved Ant Algorithms for Software Testing Cases Generation
Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mecha...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/392309 |
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| _version_ | 1832563964545859584 |
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| author | Shunkun Yang Tianlong Man Jiaqi Xu |
| author_facet | Shunkun Yang Tianlong Man Jiaqi Xu |
| author_sort | Shunkun Yang |
| collection | DOAJ |
| description | Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations. |
| format | Article |
| id | doaj-art-1bb5e2794bc4422687fbcb0ef9107417 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-1bb5e2794bc4422687fbcb0ef91074172025-02-03T01:12:03ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/392309392309Improved Ant Algorithms for Software Testing Cases GenerationShunkun Yang0Tianlong Man1Jiaqi Xu2School of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaExisting ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.http://dx.doi.org/10.1155/2014/392309 |
| spellingShingle | Shunkun Yang Tianlong Man Jiaqi Xu Improved Ant Algorithms for Software Testing Cases Generation The Scientific World Journal |
| title | Improved Ant Algorithms for Software Testing Cases Generation |
| title_full | Improved Ant Algorithms for Software Testing Cases Generation |
| title_fullStr | Improved Ant Algorithms for Software Testing Cases Generation |
| title_full_unstemmed | Improved Ant Algorithms for Software Testing Cases Generation |
| title_short | Improved Ant Algorithms for Software Testing Cases Generation |
| title_sort | improved ant algorithms for software testing cases generation |
| url | http://dx.doi.org/10.1155/2014/392309 |
| work_keys_str_mv | AT shunkunyang improvedantalgorithmsforsoftwaretestingcasesgeneration AT tianlongman improvedantalgorithmsforsoftwaretestingcasesgeneration AT jiaqixu improvedantalgorithmsforsoftwaretestingcasesgeneration |