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Improvising the effectiveness of test suites using differential evolution technique

机译：使用差分进化技术提高测试套件的有效性

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The process of testing any software system is an atrocious task which indeed consumes a ton of effort, and expensive also. Required effort and time to do adequate as well as effective testing get bigger, as the software gets more complexed that can lead to swarm over the project budget or some test cases left uncovered or delay in completion. A suitably generated test suite does not only locate errors but also aid in reducing cost investment associated with the testing process. This paper implements an optimizing technique called as Differential Evolution to improve the effectiveness of test cases using Average Percentage of Fault Detection (APFD) metric. APFD is taken as the fitness function which is to be optimized. In this work, We have performed comparison of our approach with other existing prioritizing approaches and Experimental computations show that Differential Evolution technique achieve better APFD values than other techniques.
机译：测试任何软件系统的过程都是一项艰巨的任务，确实要花费大量的精力，而且也很昂贵。随着软件变得更加复杂，这可能导致大量的项目预算或未发现的某些测试案例或延迟完成，因此进行适当的测试以及有效的测试所需的精力和时间变得越来越大。适当生成的测试套件不仅可以定位错误，还可以帮助减少与测试过程相关的成本投资。本文实现了一种称为差分进化的优化技术，以使用平均故障检测百分比（APFD）度量来提高测试用例的有效性。将APFD用作要优化的适应度函数。在这项工作中，我们将我们的方法与其他现有的优先级方法进行了比较，实验计算表明，差分进化技术比其他技术具有更好的APFD值。

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