系统综述进化算法在自动生成测试用例中的应用与挑战
Automated Unit Test Case Generation: A Systematic Literature Review
- 梳理遗传算法与粒子群优化在测试用例生成中的改进方法
- 发现混合算法、与变异测试及神经网络结合是主要优化方向
- 适合关注自动化测试、算法优化的研究者参考
软件已渗透社会各个领域，确保其质量至关重要，以避免糟糕的用户体验以及潜在的重大财务和人员损失。然而，软件测试成本高昂，耗时且占用资源。因此，过去几十年中自动化软件测试成为研究热点。本文通过系统性文献综述，发现当前在遗传算法与粒子群优化方面的改进存在知识空白，同时自动化测试面临诸多现实挑战。为此，本文总结了进化算法在测试用例生成中的现有研究成果，涵盖混合算法组合、与变异测试及神经网络的融合等改进策略，并分析了主流测试准则及其当前面临的可读性、模拟（mocking）等难题。
原文摘要 · Abstract (English)
Software is omnipresent within all factors of society. It is thus important to ensure that software are well tested to mitigate bad user experiences as well as the potential for severe financial and human losses. Software testing is however expensive and absorbs valuable time and resources. As a result, the field of automated software testing has grown of interest to researchers in past decades. In our review of present and past research papers, we have identified an information gap in the areas of improvement for the Genetic Algorithm and Particle Swarm Optimisation. A gap in knowledge in the current challenges that face automated testing has also been identified. We therefore present this systematic literature review in an effort to consolidate existing knowledge in regards to the evolutionary approaches as well as their improvements and resulting limitations. These improvements include hybrid algorithm combinations as well as interoperability with mutation testing and neural networks. We will also explore the main test criterion that are used in these algorithms alongside the challenges currently faced in the field related to readability, mocking and more.
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