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WuKong: effective diagnosis of bugs at large system scales

Published:23 February 2013Publication History
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Abstract

A key challenge in developing large scale applications (both in system size and in input size) is finding bugs that are latent at the small scales of testing, only manifesting when a program is deployed at large scales. Traditional statistical techniques fail because no error-free run is available at deployment scales for training purposes. Prior work used scaling models to detect anomalous behavior at large scales without being trained on correct behavior at that scale. However, that work cannot localize bugs automatically. In this paper, we extend that work in three ways: (i) we develop an automatic diagnosis technique, based on feature reconstruction; (ii) we design a heuristic to effectively prune the feature space; and (iii) we validate our design through one fault-injection study, finding that our system can effectively localize bugs in a majority of cases.

References

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      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 48, Issue 8
        PPoPP '13
        August 2013
        309 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/2517327
        Issue’s Table of Contents
        • cover image ACM Conferences
          PPoPP '13: Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
          February 2013
          332 pages
          ISBN:9781450319225
          DOI:10.1145/2442516

        Copyright © 2013 Authors

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 23 February 2013

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