HOLMES Effective statistical debugging via efficient path profiling

Statistical debugging aims to automate the process of isolating bugs by profiling several runs of the program and using statistical analysis to pinpoint the likely causes of failure. In this paper, we investigate the impact of using richer program profiles such as path profiles on the effectiveness...

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Bibliographic Details
Published in:2009 IEEE 31st International Conference on Software Engineering pp. 34 - 44
Main Authors: Chilimbi, Trishul M., Liblit, Ben, Mehra, Krishna, Nori, Aditya V., Vaswani, Kapil
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 16.05.2009
IEEE
Series:ACM Conferences
Subjects:
Benchmark testing
Computer bugs
Computer crashes
Debugging
General and reference > Cross-computing tools and techniques > Reliability
Instruments
Optimization
Programming profession
Sampling methods
Software and its engineering > Software creation and management > Software verification and validation > Software defect analysis > Software testing and debugging
Software and its engineering > Software organization and properties > Extra-functional properties > Software reliability
Software testing
Statistical analysis
ISBN:9781424434534, 142443453X
ISSN:0270-5257
Online Access:Get full text
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Summary:Statistical debugging aims to automate the process of isolating bugs by profiling several runs of the program and using statistical analysis to pinpoint the likely causes of failure. In this paper, we investigate the impact of using richer program profiles such as path profiles on the effectiveness of bug isolation. We describe a statistical debugging tool called HOLMES that isolates bugs by finding paths that correlate with failure. We also present an adaptive version of HOLMES that uses iterative, bug-directed profiling to lower execution time and space overheads. We evaluate HOLMES using programs from the SIR benchmark suite and some large, real-world applications. Our results indicate that path profiles can help isolate bugs more precisely by providing more information about the context in which bugs occur. Moreover, bug-directed profiling can efficiently isolate bugs with low overheads, providing a scalable and accurate alternative to sparse random sampling.
ISBN:9781424434534
142443453X
ISSN:0270-5257
DOI:10.1109/ICSE.2009.5070506