Software Fault Localization Based on Redundant Coverage Information Reduction

As the scale and complexity of software increase, it becomes more difficult to ensure its quality and reliability. Some of the most important software quality and reliability assurance methods are software fault localization techniques, of which spectrum-based fault localization (SFL) is the most commonly used. SFL calculates the suspicious values of code statements by analyzing the statement coverage matrix, locating the faulty statements according to the suspicious values. However, the statement coverage matrix suffers from a serious redundancy problem, which severely impairs the fault localization performance of SFL. For instance, in more than half of the statement coverage matrices of 395 programs in the Defects4J dataset, there are other statements with the same coverage information for 90% of the statements. Feature selection, a data preprocessing technique, is often used to obtain valuable feature subsets by removing redundant and irrelevant features. We propose a software fault localization approach, based on redundant coverage information reduction (FLRR), by taking the statement coverage matrix as the original feature set and modeling the reduction of redundant coverage information as a feature selection problem. First, feature selection techniques are applied to reduce the statement coverage matrix, which includes both statement coverage information and test case execution results, to obtain a subset of the matrix. Second, SFL is used to calculate the suspicious values of statements in the statement coverage matrix subset, and the statements are sorted in descending order according to their suspicious values. The method presented in this paper uses six common feature selection techniques to perform feature selection and reduction on the statement coverage matrix, to obtain the subset of the matrix, and then uses four typical SFL techniques to localize faulty statements in the subset. To evaluate the fault localization performance of FLRR, comparative experiments were conducted with four typical SFL techniques on the Defects4J dataset, using Einspect@n and MRR (Mean Reciprocal Rank) as evaluation metrics. Experimental results show that FLRR can improve the fault localization performance of SFL. When compared with DStar, Ochiai, Barinel, and OP2, FLRR located 23, 26, 14, and 13 more faulty statements, improved Einspect@n (n = 1) by 69.70%, 76.47%, 45.16%, and 38.24%, and improved MRR by 20.08%, 24.94%, 17.45%, and 19.15%, respectively. © 2024 Chinese Institute of Electronics. All rights reserved.