Automatic Discovery and Cleansing of Numerical Metamorphic Relations

Bo Zhang, Hongyu Zhang, Junjie Chen, Dan Hao, P. Moscato
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引用次数: 22

Abstract

Metamorphic relations (MRs) describe the invariant relationships between program inputs and outputs. By checking for violations of MRs, faults in programs can be detected. Identifying MRs manually is a tedious and error-prone task. In this paper, we propose AutoMR, a novel method for systematically inferring and cleansing MRs. AutoMR can discover various types of equality and inequality MRs through a search method (particle swarm optimization). It also employs matrix singular-value decomposition and constraint solving techniques to remove the redundant MRs in the search results. Our experiments on 37 numerical programs from two popular open source packages show that AutoMR can effectively infer a set of accurate and succinct MRs and outperform the state-of-the-art method. Furthermore, we show that the discovered MRs have high fault detection ability in mutation testing and differential testing.
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数值变质关系的自动发现与清理
变质关系(MRs)描述了程序输入和输出之间的不变关系。通过检查是否违反MRs,可以检测到程序中的故障。手动识别MRs是一项乏味且容易出错的任务。在本文中,我们提出了一种系统推断和清理MRs的新方法AutoMR。AutoMR可以通过一种搜索方法(粒子群优化)发现各种类型的相等和不相等MRs。利用矩阵奇异值分解和约束求解技术去除搜索结果中的冗余MRs。我们对来自两个流行的开源软件包的37个数值程序进行的实验表明,AutoMR可以有效地推断出一组准确而简洁的mr,并且优于最先进的方法。此外,我们还证明了所发现的MRs在突变检测和差分检测中具有较高的故障检测能力。
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