Run-Time Verification for Observational Determinism Using Dynamic Program Slicing

Ghorbani, Mohammad; Fallah, Mehran S.

doi:10.1007/978-3-319-69659-1_22

Cited by 1 publication

(1 citation statement)

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“…Fault localization techniques focus on how to use the static or dynamic information of software to accurately and quickly locate program elements that cause failures. The existing fault localization techniques can mainly be classified into seven types, including spectrum-based fault localization (SBFL) [ [8], mutation-based fault localization (MBFL) [9] [10] [11] [12], dynamic program slicing [13] [14] [15], stack trace analysis [16] [17], prediction switching [18], information retrieval-based fault localization [19], and history-based fault localization [20] [21]. Among these seven types of fault localization techniques, SBFL and MBFL are the most popular fault localization methods.…”

Section: Introductionmentioning

confidence: 99%

Improving Software Fault Localization by Combining Spectrum and Mutation

et al. 2020

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The performance of software fault localization techniques is critical to software debugging and the reliability of software. Spectrum-based fault localization (SBFL) and mutation-based fault localization (MBFL) are the two most popular fault localization methods. However, the accuracies of the two methods are still limited. For example, only 10.63% of faults can be detected by inspecting the top 3 suspicious elements reported by Ochiai, which is a famous SBFL technique. Unfortunately, programmers only examine the first few suspicious elements before losing patience. Since the information used in SBFL and MBFL are quite different and complementary, this paper proposes a novel approach by combining spectrum and mutation to improve the fault localization accuracy. First, the faulty program is evaluated by using SBFL, and the potential faulty statements are ranked according to their suspiciousness. Then, mutants of the program are generated and executed by MBFL. Finally, the statements that are ranked in the top tied n by SBFL are evaluated and reranked according to their mutation scores. Experiments are carried on the Defects4J benchmark and the results reveal that the accuracy of the proposed approach outperforms those of the SBFL and MBFL techniques. In terms of the faults located by inspecting the top 1 suspicious elements, the SMFL techniques detect at least 2.36 times more faults than two SBFL techniques (DStar and Ochiai) and detect at least 1.86 times more faults than two MBFL techniques (MUSE and Metallaxis).

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