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Conference Papers Year : 2018

Multiple Fault Localization Using Constraint Programming and Pattern Mining

Abstract

Fault localization problem is one of the most difficult processes in software debugging. The current constraint-based approaches draw strength from declarative data mining and allow to consider the dependencies between statements with the notion of patterns. Tackling large faulty programs is clearly a challenging issue for Constraint Programming (CP) approaches. Programs with multiple faults raise numerous issues due to complex dependencies between faults, making the localization quite complex for all of the current localization approaches. In this paper, we provide a new CP model with a global constraint to speed-up the resolution and we improve the localization to be able to tackle multiple faults. Finally, we give an experimental evaluation that shows that our approach improves on CP and standard approaches.
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Dates and versions

lirmm-03130609 , version 1 (03-02-2021)

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Noureddine Aribi, Mehdi Maamar, Nadjib Lazaar, Yahia Lebbah, Samir Loudni. Multiple Fault Localization Using Constraint Programming and Pattern Mining. IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2017, Boston, United States. pp.860-867, ⟨10.1109/ICTAI.2017.00134⟩. ⟨lirmm-03130609⟩
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