An evaluation of pure spectrum-based fault localization techniques for large-scale software systems

Pure spectrum-based fault localization (SBFL) is a well-studied statistical debugging technique that only takes a set of test cases (some failing and some passing) and their code coverage as input and produces a ranked list of suspicious program elements to help the developer identify the location of a bug that causes a failed test case. Studies show that pure SBFL techniques produce good ranked lists for small programs. However, our previous study based on the iBugs benchmark that uses the AspectJ repository shows that, for realistic programs, the accuracy of the ranked list is not suitable for human developers. In this paper, we confirm this based on a combined empirical evaluation with the iBugs and the Defects4J benchmark. Our experiments show that, on average, at most similar to 40%, similar to 80%, and similar to 90% of the bugs can be localized reliably within the first 10, 100, and 1000 ranked lines, respectively, in the Defects4J benchmark. To reliably localize 90% of the bugs with the best performing SBFL metric D*, similar to 450 lines have to be inspected by the developer. For human developers, this remains unsuitable, although the results improve compared with the results for the AspectJ benchmark. Based on this study, we can clearly see the need to go beyond pure SBFL and take other information, such as information from the bug report or from version history of the code lines, into consideration.