Mining behavior graphs for "Backtrace" of noncrashing bugs

Chao Liu, Xifeng Yan, Hwanjo Yu, Jiawei Han, Philip S. Yu

Research output: Contribution to conferencePaperpeer-review


Analyzing the executions of a buggy software program is essentially a data mining process. Although many interesting methods have been developed to trace crashing bugs (such as memory violation and core dumps), it is still difficult to analyze noncrashing bugs (such as logical errors). In this paper, we develop a novel method to classify the structured traces of program executions using software behavior graphs. By analyzing the correct and incorrect executions, we have made good progress at the isolation of program regions that may lead to the faulty executions. The classification framework is built on an integration of closed graph mining and SVM classification. More interestingly, suspicious regions are identified through the capture of the classification accuracy change, which is measured incrementally during program execution. Our performance study and case-based experiments show that our approach is both effective and efficient.

Original languageEnglish (US)
Number of pages12
StatePublished - 2005
Event5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States
Duration: Apr 21 2005Apr 23 2005


Other5th SIAM International Conference on Data Mining, SDM 2005
Country/TerritoryUnited States
CityNewport Beach, CA


  • Closed pattern
  • Data mining
  • Debugging
  • Noncrashing bugs
  • SVM
  • Software reliability

ASJC Scopus subject areas

  • Software


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