基于路径分析和信息熵的错误定位方法

软件错误定位是一项耗时又费力的工作，因此如何提高软件错误定位的自动化程度一直以来都是软件工程领域研究的热点.现有的基于频谱的错误定位方法很少利用程序的上下文信息，而程序的上下文信息对错误定位至关重要.针对此问题，本文提出了一种基于路径分析和信息熵的错误定位方法FLPI.该方法在基于频谱信息技术的基础上，通过对所有执行路径中的数据依赖关系进行分析来引入执行上下文信息，同时利用信息熵理论将测试事件信息引入到可疑语句的怀疑度计算公式中，以提高错误定位的精度和效率.为了评价该方法的有效性，基于一组基准程序和开源程序进行实验验证.实验结果表明，本文所提方法FLPI能够有效地提高错误定位的精度和效率.

Fault Localization Approach Based on Path Analysis and Information Entropy

Software fault localization is a time-consuming and laborious work, so determining how to improve the automation of software fault localization has always been a hot topic in the field of software engineering. The existing spectrum-based fault localization (SBFL) methods rarely use the context information of the program, which is very important for fault localization. To solve this problem, the paper proposes a fault localization approach based on path analysis and information entropy (FLPI). Based on the spectrum information technology, this approach introduces the execution context information by analyzing the data dependencies in all execution paths, and introduces the test event information into the suspiciousness formula by using the information entropy theory, so as to maximize the accuracy and efficiency of fault localization. To evaluate the effectiveness of the proposed approach, the experiments were conducted on a set of benchmark programs and open source programs. Experimental results show that our FLPI approach can effectively improve the accuracy and efficiency of fault localization.