NEAT Algorithm for Testsuite generation in Automated Software Testing

H. Raj, K. Chandrasekaran
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Abstract

Software testing is one of the most essential and an indispensable part of Software production life cycle. Software testing helps in validating if the product meets with the requirements or not, and also testing helps to validate the performance of the product. Unfortunately, this process takes up about 50% of the production time and budget, due to its laboriosity. Hence, in order to reduce the time it takes, Automated Software Testing becomes essential. Here we propose a novel idea of using Machine Learning for automatically generating the test suites. In this paper we present an approach that uses NEAT (Neuroevolution of Augmenting Topologies) Algorithm to automatically generate new test suites or for improving the coverage of already produced test suite. Our approach automatically generates test suites for white box testing. White box testing refers to testing of the internal structure and the working of the Software Under Test.
自动化软件测试中用于生成测试套件的NEAT算法
软件测试是软件生产生命周期中最重要、最不可缺少的环节之一。软件测试有助于确认产品是否满足需求,测试也有助于确认产品的性能。不幸的是,这个过程占用了大约50%的生产时间和预算,由于它的劳动。因此,为了减少所需的时间,自动化软件测试变得至关重要。在这里,我们提出了一个使用机器学习来自动生成测试套件的新想法。在本文中,我们提出了一种使用NEAT(增强拓扑的神经进化)算法来自动生成新的测试套件或改进已经生成的测试套件的覆盖率的方法。我们的方法自动为白盒测试生成测试套件。白盒测试是指对被测软件的内部结构和工作进行测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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