We introduce in our work 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 localisation 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.
Discovering the set of closed frequent patterns is one of the fundamental problems in Data Mining. Recent Constraint Programming (CP) approaches for declarative itemset mining have proven their usefulness and flexibility. But the wide use of reified constraints in current CP approaches leads to difficulties in coping with high dimensional datasets. In our work, we propose the ClosedPattern global constraint to capture the closed frequent pattern mining problem without requiring reified constraints or extra variables. We propose an algorithm to enforce domain consistency on ClosedPattern in polynomial time. The computational properties of this algorithm are analyzed and its practical effectiveness is experimentally evaluated.