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| Content Provider | ACM Digital Library |
|---|---|
| Author | Arumuga Nainar, Piramanayagam Liblit, Ben |
| Abstract | Statistical debugging uses lightweight instrumentation and statistical models to identify program behaviors that are strongly predictive of failure. However, most software is mostly correct; nearly all monitored behaviors are poor predictors of failure. We propose an adaptive monitoring strategy that mitigates the overhead associated with monitoring poor failure predictors. We begin by monitoring a small portion of the program, then automatically refine instrumentation over time to zero in on bugs. We formulate this approach as a search on the control-dependence graph of the program. We present and evaluate various heuristics that can be used for this search. We also discuss the construction of a binary instrumentor for incorporating the feedback loop into post-deployment monitoring. Performance measurements show that adaptive bug isolation yields an average performance overhead of 1% for a class of large applications, as opposed to 87% for realistic sampling-based instrumentation and 300% for complete binary instrumentation. |
| Starting Page | 255 |
| Ending Page | 264 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781605587196 |
| DOI | 10.1145/1806799.1806839 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2010-05-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Statistical debugging Dynamic feedback Control-dependence graphs Heuristic search Binary instrumentation Dyninst |
| Content Type | Text |
| Resource Type | Article |
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