FLACK:反例导向的合金模型故障定位

Guolong Zheng, Thanhvu Nguyen, Simón Gutiérrez Brida, Germán Regis, M. Frias, Nazareno Aguirre, H. Bagheri
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引用次数: 7

摘要

故障定位是一个实用的研究课题,它可以帮助开发人员识别可能导致程序错误的代码位置。大多数现有的故障定位技术都是为命令式程序(例如C和Java)设计的,并且依赖于分析程序的正确和错误执行来识别可疑语句。在这项工作中,我们为用声明性语言编写的模型引入了一种故障定位方法,其中模型不是“执行”的,而是转换为逻辑公式并使用后端约束求解器进行求解。我们介绍了FLACK,这是一个工具,它将由一些违反断言组成的Alloy模型作为输入,并返回导致断言违反的可疑表达式的排序列表。关键思想是分析反例(即不满足断言的模型实例和满足断言的模型实例)之间的差异,以查找输入模型中的可疑表达式。实验结果表明,FLACK是高效的(可以在5秒内处理复杂的、真实的、包含数千行代码的Alloy模型)、准确的(可以始终将错误表达式排在可疑列表的前1.9%)和有用的(通常可以将错误缩小到可疑表达式中的确切位置)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FLACK: Counterexample-Guided Fault Localization for Alloy Models
Fault localization is a practical research topic that helps developers identify code locations that might cause bugs in a program. Most existing fault localization techniques are designed for imperative programs (e.g., C and Java) and rely on analyzing correct and incorrect executions of the program to identify suspicious statements. In this work, we introduce a fault localization approach for models written in a declarative language, where the models are not "executed," but rather converted into a logical formula and solved using backend constraint solvers. We present FLACK, a tool that takes as input an Alloy model consisting of some violated assertion and returns a ranked list of suspicious expressions contributing to the assertion violation. The key idea is to analyze the differences between counterexamples, i.e., instances of the model that do not satisfy the assertion, and instances that do satisfy the assertion to find suspicious expressions in the input model. The experimental results show that FLACK is efficient (can handle complex, real-world Alloy models with thousand lines of code within 5 seconds), accurate (can consistently rank buggy expressions in the top 1.9% of the suspicious list), and useful (can often narrow down the error to the exact location within the suspicious expressions).
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