Software testing is considered as an essential and critical part of the software development process. To improve the efficiency of software testing, the test case prioritization (TCP) technique is usually used to preprocess the test case set, which is formulated as a single-objective or multiple-objective optimization problem and solved by swarm intelligence algorithms. In this paper, we adopted one of the state-of-art swarm intelligence algorithms– Artificial Fish School Algorithm to solve the TCP problem. Specifically, the coding method of artificial fish school was designed in combination with the test case set; the Average Percentage of Test-point Coverage and Effective Execution Time were selected to optimize the design of clustering behavior, foraging behavior and tail-chasing behavior of artificial fish school; the optimal solution was found by population iteration. Empirical evaluation was conducted to analyze the performance of the proposed method. Comparison experiments were also carried out, and the experimental results showed that in terms of both single-objective and multiple-objective, the Artificial Fish School Algorithm could better solve the TCP problems and improve the efficiency of software testing.

软件测试被认为是软件开发过程中必不可少的关键部分。为了提高软件测试的效率，通常使用测试用例优先级（TCP）技术对测试用例集进行预处理，将其表述为单目标或多目标优化问题，并通过群智能算法解决。在本文中，我们采用了最先进的群体智能算法之一——人工鱼群算法来解决 TCP 问题。具体来说，结合测试用例集设计了人工鱼群的编码方法；选取测试点覆盖的平均百分比和有效执行时间，对人工鱼群的聚类行为、觅食行为和追尾行为进行优化设计；最优解是通过种群迭代找到的。进行了实证评估以分析所提出方法的性能。还进行了对比实验，实验结果表明，无论是单目标还是多目标，人工鱼群算法都能较好地解决TCP问题，提高软件测试效率。