Failure proximity: a fault localization-based approach

  • Authors:

  • Chao Liu;Jiawei Han

  • Affiliations:

  • University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL

  • Venue:
  • Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
  • Year:
  • 2006

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Abstract

Recent software systems usually feature an automated failure reporting system, with which a huge number of failing traces are collected every day. In order to prioritize fault diagnosis, failing traces due to the same fault are expected to be grouped together. Previous methods, by hypothesizing that similar failing traces imply the same fault, cluster failing traces based on the literal trace similarity, which we call trace proximity. However, since a fault can be triggered in many ways, failing traces due to the same fault can be quite different. Therefore, previous methods actually group together traces exhibiting similar behaviors, like similar branch coverage, rather than traces due to the same fault. In this paper, we propose a new type of failure proximity, called R-Proximity, which regards two failing traces as similar if they suggest roughly the same fault location. The fault location each failing case suggests is automatically obtained with Sober, an existing statistical debugging tool. We show that with R-Proximity, failing traces due to the same fault can be grouped together. In addition, we find that R-Proximity is helpful for statistical debugging: It can help developers interpret and utilize the statistical debugging result. We illustrate the usage of R-Proximity with a case study on the grep program and some experiments on the Siemens suite, and the result clearly demonstrates the advantage of R-Proximity over trace proximity.