Fault localization using itemset mining under constraints

摘要

We introduce in this paper an itemset mining approach to tackle the fault localization problem, which is one of the most difficult processes in software debugging. We formalize the problem of fault localization as finding the k best patterns satisfying a set of constraints modelling the most suspicious statements. We use a Constraint Programming (CP) approach to model and to solve our itemset based fault localization problem. Our approach consists of two steps: (i) mining top-k suspicious suites of statements; (ii) fault localization by processing top-k patterns. Experiments performed on standard benchmark programs show that our approach enables to propose a more precise localization than a standard approach.