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Jul Aug Sep Oct 3. Nov 6. Dec Jan Feb Mar Apr 9. May Jun Oct Milpitas, CA. Jayachand Nainar lives in Milpitas, California. On the web, Jayachand goes by the alias nainar Alhambra, IL. Kiruba Nainar lives in Alhambra, Illinois.

Online, Kiruba goes by the alias kirubanainar Herndon, VA. Satish Nainar lives in Herndon, Virginia. Brookline, MA. Shanthy Nainar lives in Brookline, Massachusetts.

On the internet, Shanthy goes by the alias shanthyjnainar. Peachtree City, GA. Nadarajan Nainar lives in Peachtree City, Georgia.

On the web, Nadarajan goes by the alias nadarajan. We Found Nainar. Naina Randu - naina. Naina Rajput - naina. Nainar Shereef - nainar. Naina Rao Naina - nainarao Nainar Nellai - sumathinainarprajin.

piramanayagam arumuga nainar (arumuga)

Naina Rajput Niki - aksh. Afsal Rasheed Nainar - afsal Naina Raipur - naina. Nainar Durai - Nainar-Durai. Naina Roy - naina. Naina Randi - naina. Nainar Senthilkumar - nainar. Naina Rahman papa's angel - tanisa. Papa'S LadLi Angel. Sankar dev accadmy Muhila Home Model School. Imran Nainar Nainar Mohamed Imran - nainarimran. Naina Rawat - rakhi. Nainar Mohamed - nainar. Gayathri Nainar - gayathri. Nainar Manian - nainar. Ajmalkhan Nainar - AjmalkhanNainar.Milpitas, CA.

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Jayachand Nainar lives in Milpitas, CA. Online, Jayachand goes by the alias nainar Alhambra, IL. Kiruba Nainar lives in Alhambra, Illinois. On the web, Kiruba goes by the alias kirubanainar Herndon, VA. Satish Nainar lives in Herndon, VA. Brookline, MA. Shanthy Nainar lives in Brookline, Massachusetts. Online, Shanthy goes by the alias shanthyjnainar.

Peachtree City, GA. Online, Nadarajan goes by the alias nadarajan. We Found Nainar. Naina Randu - naina.

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Naina Rajput - naina. Nainar Shereef - nainar. Naina Rao Naina - nainarao Nainar Nellai - sumathinainarprajin. Naina Rajput Niki - aksh. Afsal Rasheed Nainar - afsal Naina Raipur - naina. Nainar Durai - Nainar-Durai. Naina Roy - naina. Naina Randi - naina. Nainar Senthilkumar - nainar.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.

Documents: Advanced Search Include Citations. Citations: 54 - 4 self. Abstract Statistical debugging uses lightweight instrumentation and statistical models to identify program behaviors that are strongly predictive of failure.

piramanayagam arumuga nainar (arumuga)

Powered by:.Statistical debugging uses dynamic instrumentation and machine learning to identify predicates on program state that are strongly predictive of program failure. Prior approaches have only considered simple, atomic predicates such as the directions of branches or the return values of function calls. We enrich the predicate vocabulary by adding complex Boolean formulae derived from these simple predicates.

We draw upon three-valued logic, static program structure, and statistical estimation techniques to efficiently sift through large numbers of candidate Boolean predicate formulae. We present qualitative and quantitative evidence that complex predicates are practical, precise, and informative. Furthermore, we demonstrate that our approach is robust in the face of incomplete data provided by the sparse random sampling that typifies postdeployment statistical debugging.

Documents: Advanced Search Include Citations. Citations: 38 - 4 self. Abstract Statistical debugging uses dynamic instrumentation and machine learning to identify predicates on program state that are strongly predictive of program failure.

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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 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. Keywords statistical debugging, binary instrumentation, dynamic feedback, heuristic search, control-dependence graphs, Dyninst 1. Enjoy affordable access to over 18 million articles from more than 15, peer-reviewed journals.

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piramanayagam arumuga nainar (arumuga)

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DOI: 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.

View on IEEE. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Figures, Tables, and Topics from this paper. Figures and Tables. Citations Publications citing this paper. Verification Reliab. References Publications referenced by this paper.

Low-overhead memory leak detection using adaptive statistical profiling Matthias HauswirthTrishul M. Hollingsworth Computer Science Int. High Perform.

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On-line automated performance diagnosis on thousands of processes Philip C. RothBarton P. Efficient online computation of statement coverage Mustafa M. TikirJeffrey K.


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