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Locating Faults with Program Slicing: An Empirical Analysis

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posted on 2023-11-29, 18:06 authored by Ezekiel Soremekun, Lukas Kirschner, Marcel Böhme, Andreas ZellerAndreas Zeller
Statistical fault localization is an easily deployed technique for quickly determining candidates for faulty code locations. If a human programmer has to search the fault beyond the top candidate locations, though, more traditional techniques of following dependencies along dynamic slices may be better suited. In a large study of 457 bugs (369 single faults and 88 multiple faults) in 46 open-source C programs, we compare the effectiveness of statistical fault localization against dynamic slicing. For single faults, we find that dynamic slicing was eight percentage points more effective than the best per- forming statistical debugging formula; for 66% of the bugs, dynamic slicing finds the fault earlier than the best performing statistical debugging formula. In our evaluation, dynamic slicing is more effective for programs with single fault, but statistical debugging performs better on multiple faults. Best results, however, are obtained by a hybrid approach: If programmers first examine at most the top five most suspicious locations from statistical debugging, and then switch to dynamic slices, on average, they will need to examine 15% (30 lines) of the code. These findings hold for 18 most effective statistical debugging formulas and our results are independent of the number of faults (i.e. single or multiple faults) and error type (i.e. artificial or real errors).

History

Preferred Citation

Ezekiel Soremekun, Lukas Kirschner, Marcel Böhme and Andreas Zeller. Locating Faults with Program Slicing: An Empirical Analysis. In: Empirical Software Engineering. 2021.

Primary Research Area

  • Secure Connected and Mobile Systems

Legacy Posted Date

2022-10-13

Journal

Empirical Software Engineering

Open Access Type

  • Green

Sub Type

  • Article

BibTeX

@article{cispa_all_3828, title = "Locating Faults with Program Slicing: An Empirical Analysis", author = "Soremekun, Ezekiel and Kirschner, Lukas and Böhme, Marcel and Zeller, Andreas", journal="{Empirical Software Engineering}", year="2021", }

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