Description
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, deeplearningPeriod | 18 Feb 2020 |
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Event title | VST@SANER 2020 |
Event type | Conference |
Location | CanadaShow on map |
Fields of science
- 102 Computer Sciences
- 102022 Software development
JKU Focus areas
- Digital Transformation
Documents & Links
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Projects
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COMET K1-Zentrum Software Competence Center Hagenberg (SCCH)
Project: Funded research › FFG - Austrian Research Promotion Agency