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引用次数: 3

摘要

由于当前基于规范的测试(SBT)在回归测试用例生成中面临一些挑战,我们已经提出了一种结合形式规范和遗传算法(GA)的测试用例生成新方法。该方法主要通过遗传算法重新配置形式规范来生成输入数据,这些数据可以杀死被测目标程序中尽可能多的突变体。在本文中,我们提出了提高该方法解搜索的可操作性和准确性的思路。具体来说,我们提出了一种使用遗传规划的规范级约束操作,并从染色体标记的清晰度和搜索解决方案的能力的角度讨论了有效性。
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Specification-based Test Case Generation with Constrained Genetic Programming
Since current specification-based testing (SBT) faces some challenges in regression test case generation, we have already proposed a new method for test case generation that combines formal specification and genetic algorithms (GA). This method mainly reconfigures formal specifications though GA to generate inputs data that can kill as many as possible mutants of the target program under test. In this paper, we propose ideas to improve the operability and the accuracy of solution search of this method. Specifically, we propose a specification-level constrained operation using genetic programming and discuss effectiveness from the viewpoint of clarity of chromosome notation and ability to search for solutions.
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