TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Strategy for Multi-target Paths Coverage by Improving Individual Information Sharing |
| 영문제목(English Title) |
A Strategy for Multi-target Paths Coverage by Improving Individual Information Sharing |
| 저자(Author) |
Zhongsheng Qian
Dafei Hong
Chang Zhao
Jie Zhu
Zhanggeng Zhu
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| 원문수록처(Citation) |
VOL 13 NO. 11 PP. 5464 ~ 5488 (2019. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The multi-population genetic algorithm in multi-target paths coverage has become a top choice for many test engineers. Also, information sharing strategy can improve the efficiency of multi-population genetic algorithm to generate multi-target test data; however, there is still space for some improvements in several aspects, which will affect the effectiveness of covering the target path set. Therefore, a multi-target paths coverage strategy is proposed by improving multi-population genetic algorithm based on individual information sharing among populations. It primarily contains three aspects. Firstly, the behavior of the sub-population covering corresponding target path is improved, so that it can continue to try to cover other sub-paths after covering the current target path, so as to take full advantage of population resources; Secondly, the populations initialized are prioritized according to the matching process, so that those sub-populations with better path coverage rate are executed firstly. Thirdly, for difficultly-covered paths, the individual chromosome features which can cover the difficultly-covered paths are extracted by utilizing the data generated, so as to screen those individuals who can cover the difficultly-covered paths. In the experiments, several benchmark programs were employed to verify the accuracy of the method from different aspects and also compare with similar methods. The experimental results show that it takes less time to cover target paths by our approach than the similar ones, and achieves more efficient test case generation process. Finally, a plug-in prototype is given to implement the approach proposed.
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| 키워드(Keyword) |
multi-population genetic algorithm
individual information sharing
multi-target paths coverage
contact layer proximity
test case
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| 파일첨부 |
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