Journal
ACM SIGPLAN NOTICES
Volume 38, Issue 5, Pages 141-154Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/780822.781148
Keywords
bug isolation; random sampling; assertions; feature selection; statistical debugging; logistic regression
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We propose a low-overhead sampling infrastructure for gathering information from the executions experienced by a program's user community. Several example applications illustrate ways to use sampled instrumentation to isolate bugs. Assertion-dense code can be transformed to share the cost of assertions among many users. Lacking assertions, broad guesses can be made about predicates that predict program errors and a process of elimination used to whittle these down to the true bug. Finally, even for non-deterministic bugs such as memory corruption, statistical modeling based on logistic regression allows us to identify program behaviors that are strongly correlated with failure and are therefore likely places to look for the error.
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