Software Fault Localization Using N-gram Analysis

Authors:
Syeda Nessa;Muhammad Abedin;W. Eric Wong;Latifur Khan;Yu Qi
Affiliations:
Department of Computer Science, The University of Texas at Dallas,;Department of Computer Science, The University of Texas at Dallas,;Department of Computer Science, The University of Texas at Dallas,;Department of Computer Science, The University of Texas at Dallas,;Department of Computer Science, The University of Texas at Dallas,
Venue:
WASA '08 Proceedings of the Third International Conference on Wireless Algorithms, Systems, and Applications
Year:
2008

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Abstract

A major portion of software development effort is spent in testing and debugging. Execution sequence collected in the testing phase can be a rich source of information for locating the fault in the program, but the exact execution sequence of a program, i.e., the actual order of execution of the statements in the program, is seldom used due to the huge volume. In this study, we apply data mining techniques on this data to reduce the debugging time by narrowing down the possible location of the fault. Our method applies N-gram analysis to rank the executable statements of a software by level of suspicion. We conducted three case studies to demonstrate the effectiveness of our proposed method. We also present comparison with other approaches, and illustrate the potential of our method.