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Automated Problem Determination Using Call-Stack Matching

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Abstract

We present an architecture and algorithms for performing automated software problem determination using call-stack matching. In an environment where software is used by a large user community, the same problem may re-occur many times. We show that this can be detected by matching the program call-stack against a historical database of call-stacks, so that as soon as the problem has been resolved once, future cases of the same or similar problems can be automatically resolved. This would greatly reduce the number of cases that need to be dealt with by human support analysts. We also show how a call-stack matching algorithm can be automatically learned from a small sample of call-stacks labeled by human analysts, and examine the performance of this learning algorithm on two different data sets.

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Authors and Affiliations

  1. IBM Lotus Notes External Support, Route 100, Somers, NY, 10589

    Mark Brodie, Sheng Ma & Leonid Rachevsky

  2. IBM TJ Watson Research Center, 19 Skyline Drive, Hawthorne, NY, 10532

    Jon Champlin

Authors
  1. Mark Brodie
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  2. Sheng Ma
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  3. Leonid Rachevsky
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  4. Jon Champlin
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Corresponding author

Correspondence to Mark Brodie.

Additional information

Mark Brodie is a research staff member in the “Machine Learning for Systems” group at the IBM T.J. Watson Research Center in Hawthorne, NY. He did his undergraduate work in Mathematics at the University of the Witwatersrand in South Africa and received his PhD in Computer Science at the University of Illinois in 2000. His research interests include machine learning, data mining, and problem determination.

Sheng Ma received his BS degree in Electrical Engineering from Tsinghua University, Beijing China, in 1992, and his MS and PhD with honors in Electrical Engineering from Rensselaer Polytechnic Institute, Troy, NY, in 1995 and 1998, respectively. He joined the IBM T.J. Watson Research Center as a research staff member in 1998 and became manager of the “Machine Learning for Systems” group in 2001. His current research interests include machine learning, data mining, network traffic modeling and control, and network and computer systems management.

Leonid Rachevsky is a software systems analyst in the “Machine Learning for Systems” group at the IBM T.J. Watson Research Center in Hawthorne, NY. He obtained his MSc in Mathematics at Kazan State University and his PhD in Technical Science (Applied Mathematics) at the Kazan Institute of Chemical Engineering in Kazan, USSR (now Russia). He has worked extensively as a software engineer and senior software analyst in Israel, Canada and the United States.

Jon Champlin is an advisory software engineer with the Lotus division of IBM’s Software Group. He received a Bachelors of Computer Science from Siena College in 1993. He is part of the external support group and has developed several serviceability features for Lotus Notes/Domino.

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Brodie, M., Ma, S., Rachevsky, L. et al. Automated Problem Determination Using Call-Stack Matching. J Netw Syst Manage 13, 219–237 (2005). https://doi.org/10.1007/s10922-005-4443-8

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  • DOI: https://doi.org/10.1007/s10922-005-4443-8

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