Une approche CSP pour l'aide \`a la localisation d'erreurs

TL;DR

This paper presents a new constraint programming approach for error localization in programs using counter-examples, by analyzing paths with potential errors and computing minimal correction sets to identify likely error sources.

Contribution

It introduces a novel CP-based method for error localization that handles numerical statements efficiently and extends existing algorithms for better performance.

Findings

Preliminary results are promising and show potential effectiveness.

The approach can identify minimal correction sets for paths with errors.

It extends previous algorithms to handle numerical constraints more efficiently.

Abstract

We introduce in this paper a new CP-based approach to support errors location in a program for which a counter-example is available, i.e. an instantiation of the input variables that violates the post-condition. To provide helpful information for error location, we generate a constraint system for the paths of the CFG (Control Flow Graph) for which at most k conditional statements may be erroneous. Then, we calculate Minimal Correction Sets (MCS) of bounded size for each of these paths. The removal of one of these sets of constraints yields a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the post condition. We extend the algorithm proposed by Liffiton and Sakallah \cite{LiS08} to handle programs with numerical statements more efficiently. We present preliminary experimental results that are quite encouraging.