Refining Fitness Functions in Test-Based Program Repair

J. Petke, Aymeric Blot
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引用次数: 8

Abstract

Genetic improvement has proved to be a successful technique in optimising various software properties, such as bug fixing, runtime improvement etc. It uses automated search to find improved program variants. Usually the evaluation of each mutated program involves running a test suite, and then calculating the fitness based on Boolean test case results. This, however, creates plateaus in the fitness landscape that are hard for search to efficiently traverse. Therefore, we propose to consider a more fine-grained fitness function that takes the output of test case assertions into account.
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基于测试的程序修复中适应度函数的改进
遗传改进已被证明是一种成功的技术,可以优化各种软件属性,如bug修复、运行时改进等。它使用自动搜索来查找改进的程序变体。通常,对每个突变程序的评估包括运行一个测试套件,然后根据布尔测试用例结果计算适应度。然而,这造成了健身领域的停滞期,搜索很难有效地遍历。因此,我们建议考虑一个更细粒度的适应度函数,它将测试用例断言的输出考虑在内。
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