EvoSpex: An Evolutionary Algorithm for Learning Postconditions (artifact)

F. Molina, Pablo Ponzio, Nazareno Aguirre, M. Frias
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Abstract

Having the expected behavior of software specified in a formal language can greatly improve the automation of software verification activities, since these need to contrast the intended behavior with the actual software implementation. Unfortunately, software many times lacks such specifications, and thus providing tools and techniques that can assist developers in the construction of software specifications are relevant in software engineering. As an aid in this context, we present EvoSpex, a tool that given a Java method, automatically produces a specification of the method's current behavior, in the form of postcondition assertions. EvoSpex is based on generating software runs from the implementation (valid runs), making modifications to the runs to build divergent behaviors (invalid runs), and executing a genetic algorithm that tries to evolve a specification to satisfy the valid runs, and leave out the invalid ones. Our tool supports a rich JML-like assertion language, that can capture complex specifications, including sophisticated object structural properties.
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EvoSpex:学习后置条件(工件)的进化算法
用正式语言指定软件的预期行为可以极大地改进软件验证活动的自动化,因为这些需要将预期的行为与实际的软件实现进行对比。不幸的是,软件很多时候缺乏这样的规范,因此提供能够帮助开发人员构建软件规范的工具和技术是与软件工程相关的。为了在此上下文中提供帮助,我们介绍了EvoSpex,这是一个给定Java方法的工具,它以后置条件断言的形式自动生成方法当前行为的规范。EvoSpex基于从实现中生成软件运行(有效运行),对运行进行修改以构建不同的行为(无效运行),并执行一个遗传算法,该算法试图进化一个规范以满足有效运行,并忽略无效运行。我们的工具支持丰富的类似于xml的断言语言,可以捕获复杂的规范,包括复杂的对象结构属性。
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