Adaptive bug isolation

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 tha...

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Bibliographic Details
Published in:2010 ACM/IEEE 32nd International Conference on Software Engineering Vol. 1; pp. 255 - 264
Main Authors: Arumuga Nainar, Piramanayagam, Liblit, Ben
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 01.05.2010
IEEE
Series:ACM Conferences
Subjects:
binary instrumentation
Computer crashes
Computing methodologies > Artificial intelligence > Planning and scheduling
Computing methodologies > Artificial intelligence > Search methodologies
Computing methodologies > Artificial intelligence > Search methodologies > Discrete space search
Computing methodologies > Artificial intelligence > Search methodologies > Game tree search
Computing methodologies > Artificial intelligence > Search methodologies > Heuristic function construction
control-dependence graphs
Debugging
dynamic feedback
dyninst
heuristic search
Information systems > Data management systems > Middleware for databases > Distributed transaction monitors
Instruments
Monitoring
Optimization
Radiation detectors
Software
Software and its engineering > Software creation and management > Software verification and validation > Process validation > Traceability
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 > Contextual software domains > Operating systems > Process management > Monitors
statistical debugging
Theory of computation > Semantics and reasoning > Program reasoning > Program verification
control-dependence graphs
dynamic feedback
dyninst
statistical debugging
binary instrumentation
heuristic search
ISBN:9781605587196, 1605587192
ISSN:0270-5257
Online Access:Get full text
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Description
Summary: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.
ISBN:9781605587196
1605587192
ISSN:0270-5257
DOI:10.1145/1806799.1806839