The Probabilistic Program Dependence Graph and Its Application to Fault Diagnosis

Authors:
George K. Baah;Andy Podgurski;Mary Jean Harrold
Affiliations:
Georgia Institute of Technology, Atlanta;Case Western Reserve University, Cleveland;Georgia Institute of Technology, Atlanta
Venue:
IEEE Transactions on Software Engineering
Year:
2010

Citing 0
Cited 6

Reducing confounding bias in predicate-level statistical debugging metrics

Proceedings of the 34th International Conference on Software Engineering
Quantitative program dependence graphs

ICFEM'12 Proceedings of the 14th international conference on Formal Engineering Methods: formal methods and software engineering
Quantitative program slicing: separating statements by relevance

Proceedings of the 2013 International Conference on Software Engineering
A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media

Journal of Computational Physics
A test-suite reduction approach to improving fault-localization effectiveness

Computer Languages, Systems and Structures
Slice-based statistical fault localization

Journal of Systems and Software

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.