Issue No. 04 - July/August (2010 vol. 36)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TSE.2009.87
George K. Baah , Georgia Institute of Technology, Atlanta
Andy Podgurski , Case Western Reserve University, Cleveland
Mary Jean Harrold , Georgia Institute of Technology, Atlanta
This paper presents an innovative model of a program's internal behavior over a set of test inputs, called the probabilistic program dependence graph (PPDG), which facilitates probabilistic analysis and reasoning about uncertain program behavior, particularly that associated with faults. The PPDG construction augments the structural dependences represented by a program dependence graph with estimates of statistical dependences between node states, which are computed from the test set. The PPDG is based on the established framework of probabilistic graphical models, which are used widely in a variety of applications. This paper presents algorithms for constructing PPDGs and applying them to fault diagnosis. The paper also presents preliminary evidence indicating that a PPDG-based fault localization technique compares favorably with existing techniques. The paper also presents evidence indicating that PPDGs can be useful for fault comprehension.
Probabilistic graphical models, machine learning, fault diagnosis, program analysis.
A. Podgurski, G. K. Baah and M. J. Harrold, "The Probabilistic Program Dependence Graph and Its Application to Fault Diagnosis," in IEEE Transactions on Software Engineering, vol. 36, no. , pp. 528-545, 2009.