Fuzzy clustering the backward dynamic slices of programs to identify the origins of failure

  • Authors:

  • Saeed Parsa;Farzaneh Zareie;Mojtaba Vahidi-Asl

  • Affiliations:

  • Department of Software Engineering, Iran University of Science and Technology, Tehran, Iran;Department of Software Engineering, Iran University of Science and Technology, Tehran, Iran;Department of Software Engineering, Iran University of Science and Technology, Tehran, Iran

  • Venue:
  • SEA'11 Proceedings of the 10th international conference on Experimental algorithms
  • Year:
  • 2011

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Abstract

In this paper a new technique for identifying the origins of program failure is presented. To achieve this, the outstanding features of both statistical debugging and dynamic slicing techniques are combined. The proposed Fuzzy-Slice technique, computes the full backward dynamic slice of variables used in output statement of a given program in several failing and passing executions. According to the statements presented in the slice of an execution, each run could be converted into an execution point within Euclidean space, namely execution space. Using fuzzy clustering technique, different program execution paths are identified and the fault relevant statements are ranked according to their presence in different clusters. The novel scoring method for identifying fault relevant statements considers the observation of a statement in all execution paths. The promising results on Siemens test suite reveal the high accuracy and precision of the proposed Fuzzy-Slice technique.