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Concolic Testing in CLP

Published online by Cambridge University Press:  21 September 2020

FRED MESNARD ,
ÉTIENNE PAYET  and
GERMÁN VIDAL Open the ORCID record for GERMÁN VIDAL [Opens in a new window]
FRED MESNARD
Affiliation:
LIM - Université de la Réunion, France (e-mail: frederic.mesnard@univ-reunion.fr, etienne.payet@univ-reunion.fr)
ÉTIENNE PAYET
Affiliation:
LIM - Université de la Réunion, France (e-mail: frederic.mesnard@univ-reunion.fr, etienne.payet@univ-reunion.fr)
GERMÁN VIDAL
Affiliation:
MiST, VRAIN, Universitat Politècnica de València (e-mail: gvidal@dsic.upv.es)
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Abstract

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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.

Keywords

CLPverificationconcolic testing

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 5: 36th International Conference on Logic Programming Special Issue I , September 2020 , pp. 671 - 686
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

Footnotes

*

This author has been partially supported by EU (FEDER) and Spanish MCI/AEI under grants TIN2016-76843-C4-1-R and PID2019-104735RB-C41, and by the Generalitat Valenciana under grant Prometeo/2019/098 (DeepTrust).

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