Concolic Testing in CLP

TL;DR

This paper introduces a sound and efficient concolic testing framework for Constraint Logic Programming (CLP), enabling the handling of both positive and negative constraints, which enhances bug detection and test case generation.

Contribution

It presents a novel, generalized concolic testing framework for CLP that overcomes previous limitations by supporting negative constraints, improving soundness and efficiency.

Findings

Framework supports both positive and negative constraints

Enhanced bug detection and test case generation

Potential for increased efficiency in CLP testing

Abstract

Concolic testing is a popular software verification technique based on a combination of concrete and symbolic execution. Its main focus is finding bugs and generating test cases with the aim of maximizing code coverage. A previous approach to concolic testing in logic programming was not sound because it only dealt with positive constraints (by means of substitutions) but could not represent negative constraints. In this paper, we present a novel framework for concolic testing of CLP programs that generalizes the previous technique. In the CLP setting, one can represent both positive and negative constraints in a natural way, thus giving rise to a sound and (potentially) more efficient technique. Defining verification and testing techniques for CLP programs is increasingly relevant since this framework is becoming popular as an intermediate representation to analyze programs written in other programming paradigms.