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.

本研究针对软件测试领域中的自动化测试面临的挑战和改进方案进行了系统综述，特别关注遗传算法和粒子群优化方法的信息缺口。通过整合现有文献，本文提出了混合算法组合及其与突变测试和神经网络的互操作性，揭示了当前测试标准及领域内普遍存在的可读性和模拟问题等挑战。 

自动化单元测试用例生成：系统文献综述