by Hannes Thaller, Lukas Linsbauer, Alexander Egyed, Stefan Fischer
Abstract:
Software testing helps developers to identify bugs.However, awareness of bugs is only the first step. Finding andcorrecting the faulty program components is equally hard andessential for high-quality software. Fault localization automati-cally pinpoints the location of an existing bug in a program. It isa hard problem, and existing methods are not yet precise enoughfor widespread industrial adoption. We propose fault localizationvia Probabilistic Software Modeling (PSM). PSM analyzes thestructure and behavior of a program and synthesizes a networkof Probabilistic Models (PMs). Each PM models a method withits inputs and outputs and is capable of evaluating the likelihoodof runtime data. We use this likelihood evaluation to find faultlocations and their impact on dependent code elements. Resultsindicate that PSM is a robust framework for accurate faultlocalization.Index Terms—fault localization, probabilistic modeling, multi-variate testing, software modeling, static code analysis, dynamiccode analysis, runtime monitoring, inference, simulation, deeplearning
Reference:
Towards Fault Localization via Probabilistic Software Modeling (Hannes Thaller, Lukas Linsbauer, Alexander Egyed, Stefan Fischer), IEEE, 2020.
Bibtex Entry:
@Workshop{DBLP:conf/wcre/ThallerLE020,
author = {Hannes Thaller and Lukas Linsbauer and Alexander Egyed and Stefan Fischer},
booktitle = {{IEEE} Workshop on Validation, Analysis and Evolution of Software Tests, VST@SANER 2020, London, ON, Canada, February 18, 2020},
title = {Towards Fault Localization via Probabilistic Software Modeling},
year = {2020},
pages = {24--27},
publisher = {{IEEE}},
abstract = {Software testing helps developers to identify bugs.However, awareness of bugs is only the first step. Finding andcorrecting the faulty program components is equally hard andessential for high-quality software. Fault localization automati-cally pinpoints the location of an existing bug in a program. It isa hard problem, and existing methods are not yet precise enoughfor widespread industrial adoption. We propose fault localizationvia Probabilistic Software Modeling (PSM). PSM analyzes thestructure and behavior of a program and synthesizes a networkof Probabilistic Models (PMs). Each PM models a method withits inputs and outputs and is capable of evaluating the likelihoodof runtime data. We use this likelihood evaluation to find faultlocations and their impact on dependent code elements. Resultsindicate that PSM is a robust framework for accurate faultlocalization.Index Terms—fault localization, probabilistic modeling, multi-variate testing, software modeling, static code analysis, dynamiccode analysis, runtime monitoring, inference, simulation, deeplearning},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/wcre/ThallerLE020.bib},
doi = {10.1109/VST50071.2020.9051635},
file = {:Conferences/SANER 2020 - Towards Fault Localization via Probabilistic Software Modeling/Towards Fault Localization via Probabilistic Software Modeling-preprint.pdf:PDF},
keywords = {SCCH},
timestamp = {Thu, 16 Apr 2020 16:01:21 +0200},
url = {https://doi.org/10.1109/VST50071.2020.9051635},
}