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Multi-task modeling and multifactorial optimization for path coverage problem of automated test case generation
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AI
DOI：10.1016/j.asoc.2024.111407.png)
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摘要
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Recent research in automated test case generation (ATCG) focuses on multi -objective optimization using functions based on path structure (F -PS) to solve the path coverage (PC) problem. Despite the similarity among F-PSs, the existing multi -objective optimization models fail to consider using the similarity to effectively promote optimization among multiple objectives. Inspired by the similarity and multitask optimization, this paper first establishes a multitasking path coverage (MtPC) model with two different F-PSs as its tasks. A multifactorial optimization framework for solving MtPC model (MfO-PC) is then proposed to optimize the tasks by assortative mating and to cooperatively generate desired test cases by automatic assignment strategy. Three multifactorial optimization algorithms based on the framework are then designed and tested on twelve benchmark programs. Experimental results show that the effectiveness of the proposed model and the designed algorithms based on MfO-PC framework achieve the highest path coverage with fewer test cases and less running time than some compared state-of-the-art algorithms.
Keyword:
Automated test case generation for path
coverage
Functions based on path structure
Multitask optimization
Automatic assignment strategy
Assortative mating
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimization.png)
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