Context-aware statistical debugging: From bug predictors to faulty control flow paths

Effective bug localization is important for realizing automated debugging. One attractive approach is to apply statistical techniques on a collection of evaluation profiles of program properties to help localize bugs. Previous research has proposed various specialized techniques to isolate certain p...

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Main Authors: JIANG, Lingxiao, SU, Zhendong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2007
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Online Access:https://ink.library.smu.edu.sg/sis_research/945
https://ink.library.smu.edu.sg/context/sis_research/article/1944/viewcontent/ContextAwareStatDebugging_2007.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-19442017-02-05T02:10:12Z Context-aware statistical debugging: From bug predictors to faulty control flow paths JIANG, Lingxiao SU, Zhendong Effective bug localization is important for realizing automated debugging. One attractive approach is to apply statistical techniques on a collection of evaluation profiles of program properties to help localize bugs. Previous research has proposed various specialized techniques to isolate certain program predicates as bug predictors. However, because many bugs may not be directly associated with these predicates, these techniques are often ineffective in localizing bugs. Relevant control flow paths that may contain bug locations are more informative than stand-alone predicates for discovering and understanding bugs. In this paper, we propose an approach to automatically generate such faulty control flow paths that link many bug predictors together for revealing bugs. Our approach combines feature selection (to accurately select failure-related predicates as bug predictors), clustering (to group correlated predicates), and control flow graph traversal in a novel way to help generate the paths. We have evaluated our approach on code including the Siemens test suite and rhythmbox (a large music management application for GNOME). Our experiments show that the faulty control flow paths are accurate, useful for localizing many bugs, and helped to discover previously unknown errors in rhythmbox 2007-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/945 info:doi/10.1145/1321631.1321660 https://ink.library.smu.edu.sg/context/sis_research/article/1944/viewcontent/ContextAwareStatDebugging_2007.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University bug localization machine learning statistical debugging control flow analysis Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic bug localization
machine learning
statistical debugging
control flow analysis
Software Engineering
spellingShingle bug localization
machine learning
statistical debugging
control flow analysis
Software Engineering
JIANG, Lingxiao
SU, Zhendong
Context-aware statistical debugging: From bug predictors to faulty control flow paths
description Effective bug localization is important for realizing automated debugging. One attractive approach is to apply statistical techniques on a collection of evaluation profiles of program properties to help localize bugs. Previous research has proposed various specialized techniques to isolate certain program predicates as bug predictors. However, because many bugs may not be directly associated with these predicates, these techniques are often ineffective in localizing bugs. Relevant control flow paths that may contain bug locations are more informative than stand-alone predicates for discovering and understanding bugs. In this paper, we propose an approach to automatically generate such faulty control flow paths that link many bug predictors together for revealing bugs. Our approach combines feature selection (to accurately select failure-related predicates as bug predictors), clustering (to group correlated predicates), and control flow graph traversal in a novel way to help generate the paths. We have evaluated our approach on code including the Siemens test suite and rhythmbox (a large music management application for GNOME). Our experiments show that the faulty control flow paths are accurate, useful for localizing many bugs, and helped to discover previously unknown errors in rhythmbox
format text
author JIANG, Lingxiao
SU, Zhendong
author_facet JIANG, Lingxiao
SU, Zhendong
author_sort JIANG, Lingxiao
title Context-aware statistical debugging: From bug predictors to faulty control flow paths
title_short Context-aware statistical debugging: From bug predictors to faulty control flow paths
title_full Context-aware statistical debugging: From bug predictors to faulty control flow paths
title_fullStr Context-aware statistical debugging: From bug predictors to faulty control flow paths
title_full_unstemmed Context-aware statistical debugging: From bug predictors to faulty control flow paths
title_sort context-aware statistical debugging: from bug predictors to faulty control flow paths
publisher Institutional Knowledge at Singapore Management University
publishDate 2007
url https://ink.library.smu.edu.sg/sis_research/945
https://ink.library.smu.edu.sg/context/sis_research/article/1944/viewcontent/ContextAwareStatDebugging_2007.pdf
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