Causal Program Dependence Analysis

TL;DR

CPDA is a dynamic dependence analysis method that uses causal inference to model program element dependencies, requiring only lightweight parsing and effectively aiding fault localization.

Contribution

Introduces CPDA, a novel causal inference-based dynamic dependence analysis technique that improves fault localization with minimal static analysis.

Findings

CPDA effectively captures key dependence relationships.

CPDA doubles the fault localization capability compared to SBFL.

CPDA requires only lightweight parsing for analysis.

Abstract

We introduce Causal Program Dependence Analysis (CPDA), a dynamic dependence analysis that applies causal inference to model the strength of program dependence relations in a continuous space. CPDA observes the association between program elements by constructing and executing modified versions of a program. One advantage of CPDA is that this construction requires only light-weight parsing rather than sophisticated static analysis. The result is a collection of observations based on how often a change in the value produced by a mutated program element affects the behavior of other elements. From this set of observations, CPDA discovers a causal structure capturing the causal (i.e., dependence) relation between program elements. Qualitative evaluation finds that CPDA concisely expresses key dependence relationships between program elements. As an example application, we apply CPDA to the problem of fault localization. Using minimal test suites, our approach can rank twice as many faults compared to SBFL.