使用混合方法生成基于突变的测试数据

Seifu Detso Bejo, Beakal Gizachew Assefa, Sudhir Kumar Mohapatra
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引用次数: 0

摘要

基于故障的测试是一种通过评估测试套件的有效性来确保软件质量的强大技术,也用于检查其他软件测试技术执行的测试的彻底性。然而,它是一种非常复杂且计算量昂贵的测试方法。文献表明,在给出形式化解决方案和启发式方法方面付出了巨大的努力。最近,基于混合优化技术的最先进方法已被证明适合于具有成本效益的结果。本文将回溯搜索优化算法与整数规划方法(BackIP)相结合,实现并提出了一种多目标的新型混合算法。与其他方法不同,BackIP是一种测试输入数据生成方法,它同时包括测试数据生成、突变分析和测试套件缩减。在一个广泛使用的基准java程序上进行了实验比较,结果表明,与目前的技术相比,所提出的方法实现了突变分数高达94%的测试数据生成,改进的测试套件减少了70%至94%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BackIP: Mutation Based Test Data Generation Using Hybrid Approach
Fault-based testing is a powerful technique to ensure the quality of software by evaluating the efficacy of the test suits and also used to check the thoroughness of testing performed by other software testing techniques. However, it is very complicated and computationally expensive testing method. Literature shows that there is a tremendous effort to give formal solutions and heuristics methods. Recently, state-of-the-art approaches based on hybrid optimization techniques have been proven to be suitable for cost effective results. This work implements and presents a multi-objective novel hybrid method by combining Backtracking search optimization algorithm and Integer programming approach(BackIP). Unlike, some other approaches, BackIP is a test input data generation method which includes test data generation, mutation analysis, and test suite reduction simultaneously. Experimental comparison is conducted on a widely used benchmark java programs and results show that the proposed approach achieves test data generation with mutation score up to 94% and improved test suite reduction between 70% to 94% as compared to the state-of-the-art techniques.
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