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ICSE
2009
IEEE-ACM

HOLMES: Effective statistical debugging via efficient path profiling

8 years 9 months ago
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 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.
Trishul M. Chilimbi, Ben Liblit, Krishna K. Mehra,
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICSE
Authors Trishul M. Chilimbi, Ben Liblit, Krishna K. Mehra, Aditya V. Nori, Kapil Vaswani
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