CRMF: A fault localization approach based on class reduction and method call frequency. (20th December 2022)

• Record Type: Journal Article
• Authors: Liu, Hengyuan; Li, Zheng; Wang, Haifeng; Liu, Yong; Chen, Xiang
• Is Part Of: Software, practice & experience. Volume 53:Number 4(2023)
• Page Start: 1061
• Page End: 1090
• Publication Date: 2022-12-20
• Subjects: class reduction -- fault localization -- information retrieval -- method call frequency -- program debugging; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3178
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Identifying the location of faults in real‐world programs is one of the costly processes during software debugging. To reduce the debugging effort, various fault localization techniques have been proposed in recent years. Spectrum‐based fault localization (SBFL) is one kind of widely investigated fault localization technique. Most SBFL techniques first calculate the suspiciousness of program elements (such as statements, methods) to be faulty using the coverage information and execution results of tests. Then a rank list of program elements is generated according to their suspiciousness. However, some SBFL techniques only consider the binary coverage information (i.e., whether the program element is covered) but ignore some of the tests' running behaviors, such as the execution frequency when faults occur in the iteration entities or loop bodies, which are more likely to be faulty followed the propagation‐infection‐execution model. The execution frequency based techniques only replace the feature items of the existing formula limiting their effectiveness in fault localization. In this article, we propose a fault localization technique, class reduction and method call frequency (CRMF), which utilizes mutation analysis and information retrieval techniques. In particular, CRMF first uses mutation analysis to identify and reduce the classes, in which the program elements with a low probability of being faulty. Then we propose a new suspiciousness formula that applies information retrieval and considers method call frequency. To evaluate the effectiveness of CRMF, we conduct empirical studies on 264 real‐world programs from the Defects4J benchmark. Final results show that CRMF outperforms the statement frequency based technique FLSF and SBFL techniques (i.e., Ochiai, OP2, Tarantula, and Dstar) in both single‐fault programs and multiple‐fault programs. Specifically, CRMF can rank 29, 74, and 112 faults at the top 1, 3, 5 ranks and achieve a higher mean reciprocal rank for single‐fault programs and multiple‐fault programs. Finally, we discuss the essence of CRMF and analyze its effectiveness on multi‐fault programs in detail.