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A comparative study of Bat and Cuckoo search algorithm for regression test case selection

机译:Bat和Cuckoo搜索算法用于回归测试用例选择的比较研究

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Enhancing the software by either adding new functionality or deleting some obsolete capability or fixing the errors is called software maintenance. As a result, the software may function improperly or unchanged parts of the software may be adversely affected. Testing carried out to validate that no new errors have been introduced during maintenance activity is called Regression Testing. It is acknowledged to be an expensive activity and may account for around 60-70% of the total software life cycle cost. Reducing the cost of regression testing is therefore of vital importance and has the caliber to reduce the cost of maintenance also. This paper evaluates the performance of two metaheuristic algorithms-Bat Algorithm and Cuckoo Search Algorithm for selecting test cases. Factors that we have considered for performance evaluation are the number of faults detected and the execution time. The domain of study is the flex object from the Benchmark repository - Software Artifact and Infrastructure Repository. Extensive experiments have been conducted to collect and analyze the results. A Statistical test, F-test has also been conducted to validate the research hypothesis. Results indicate that the Cuckoo Search Algorithms perform a little better than Bat Algorithm.
机译:通过添加新功能或删除某些过时功能或修复错误来增强软件的功能称为软件维护。结果,软件可能无法正常运行,或者软件的未更改部分可能受到不利影响。为验证维护活动期间未引入新错误而进行的测试称为回归测试。它被认为是一项昂贵的活动,可能占软件生命周期总成本的60-70%。因此,降低回归测试的成本至关重要,并且具有降低维护成本的能力。本文评估了两种元启发式算法-蝙蝠算法和布谷鸟搜索算法在选择测试用例中的性能。我们考虑进行性能评估的因素是检测到的故障数和执行时间。研究的领域是Benchmark资料库-Software Artifact和Infrastructure资料库中的flex对象。已经进行了广泛的实验以收集和分析结果。还进行了统计检验,即F检验,以验证研究假设。结果表明,布谷鸟搜索算法的性能优于蝙蝠算法。

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