Fault Localization for Java Programs using Probabilistic Program Dependence Graph

TL;DR

This paper introduces Probabilistic Program Dependence Graph (PPDG), a novel model for fault localization in Java programs that combines structural and statistical dependencies to identify bugs more effectively.

Contribution

It proposes the PPDG model that enhances traditional Program Dependence Graphs with probabilistic dependencies, improving fault localization accuracy.

Findings

PPDG effectively identifies fault locations in Java programs.

The method outperforms existing fault localization techniques.

Algorithms for constructing and applying PPDG are developed.

Abstract

Fault localization is a process to find the location of faults. It determines the root cause of the failure. It identifies the causes of abnormal behaviour of a faulty program. It identifies exactly where the bugs are. Existing fault localization techniques are Slice based technique, Program- Spectrum based Technique, Statistics Based Technique, Program State Based Technique, Machine learning based Technique and Similarity Based Technique. In the proposed method Model Based Fault Localization Technique is used, which is called Probabilistic Program Dependence Graph . Probabilistic Program Dependence Graph (PPDG) is an innovative model that scans the internal behaviour of the project. PPDG construction is enhanced by Program Dependence Graph (PDG). PDG is achieved by the Control Flow Graph (CFG). 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. This work presents algorithms for constructing PPDGs and applying fault localization.