结合基于搜索的测试和基于语法的模糊测试生成高度结构化的输入数据

Mitchell Olsthoorn, A. Deursen, Annibale Panichella
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引用次数: 12

摘要

软件测试是一项重要且耗时的任务,通常是手工完成的。在过去的几十年里,研究人员已经提出了生成输入数据(例如,模糊测试)和自动化生成测试用例过程(例如,基于搜索的测试)的技术。然而,已知这些技术有其自身的局限性:基于搜索的测试不能生成高度结构化的数据;基于语法的模糊测试不会生成测试用例结构。为了解决这些限制,我们将这两种技术结合起来。通过将基于语法的突变应用到由基于搜索的测试算法收集的输入数据,它允许我们共同发展测试用例生成的两个方面。我们通过对来自三个最流行的JSON解析器的20个Java类在多个搜索预算中执行实证研究来评估我们的方法G-EVOSUITE。我们的结果表明,建议的方法平均将JSON相关类的分支覆盖率提高了15%(最大增幅为50%),而不会对其他类产生负面影响。
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Generating Highly-structured Input Data by Combining Search-based Testing and Grammar-based Fuzzing
Software testing is an important and time-consuming task that is often done manually. In the last decades, researchers have come up with techniques to generate input data (e.g., fuzzing) and automate the process of generating test cases (e.g., search-based testing). However, these techniques are known to have their own limitations: search-based testing does not generate highly-structured data; grammar-based fuzzing does not generate test case structures. To address these limitations, we combine these two techniques. By applying grammar-based mutations to the input data gathered by the search-based testing algorithm, it allows us to co-evolve both aspects of test case generation. We evaluate our approach, called G-EVOSUITE, by performing an empirical study on 20 Java classes from the three most popular JSON parsers across multiple search budgets. Our results show that the proposed approach on average improves branch coverage for JSON related classes by 15 % (with a maximum increase of 50 %) without negatively impacting other classes.
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