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Improving data-flow analysis with path profiles
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A framework for reducing the cost of instrumented code
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Visualization of test information to assist fault localization
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From symptom to cause: localizing errors in counterexample traces
POPL '03 Proceedings of the 30th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Bug isolation via remote program sampling
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Measuring Coupling and Cohesion of Software Modules: An Information-Theory Approach
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
Scalable statistical bug isolation
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SOBER: statistical model-based bug localization
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Empirical evaluation of the tarantula automatic fault-localization technique
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Preferential path profiling: compactly numbering interesting paths
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The Dynamic Function Coupling Metric and Its Use in Software Evolution
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Statistical debugging using compound boolean predicates
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Context-aware statistical debugging: from bug predictors to faulty control flow paths
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Capturing propagation of infected program states
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Scalable temporal order analysis for large scale debugging
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Fault localization through evaluation sequences
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Practical fault localization for dynamic web applications
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Precise calling context encoding
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Non-parametric statistical fault localization
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Scalable graph analyzing approach for software fault-localization
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Statistical debugging with elastic predicates
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Journal of Systems and Software
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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.