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La différence entre la théorie et la pratique, c'est qu'en théorie, il n'y a pas de différence entre la théorie et la pratique, mais qu'en pratique, il y en a une." Jan van de Sneptscheut.
About me.
I'am postdoc in CRIL laboratory in University of Artois - Lens - France. I did my PhD in University of Oran1 – LITIO - laboratory (pco team) and University of Montpellier – LIRMM - laboratory (coconut team).
Curriculum Vitae
My thesis was supervised by Yahia Lebbah and Nadjib Lazaar. My PhD thesis focus on Itemsets Mining using Constraint Programming with the aim to assist Fault Localization task in software testing process.
News :
Research interests
My current research lies in the area of:- Data mining (itemset mining, declarative mining)
- Constraint programming (modelling, global constraint)
- Software testing (faults localization, test cases)
Abstract 1
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.
Abstract 2
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.
Publications
Journals
International Conferences
- N. Aribi, M. Maamar, N. Lazaar, Y. Lebbah, S. Loudni . Multiple fault localization using constraint programming and pattern mining ICTAI’2017 - Boston - MA - USA [paper] [slides]
- N. Lazaar, S. Loudni, Y. Lebbah, M. Maamar, V. Lemière, C. Bessiere, P. Boizumault. A global constraint for Closed Frequent Pattern Mining. CP’2016 - Toulouse - France [paper] [slides]
- M. Maamar, N. Lazaar, S. Loudni, Y. Lebbah. Localisation de fautes à l’aide de la fouille de données sous contraintes. COSI’2015 - Oran - Algérie . [ paper ] [slides]
National Conferences
- M. Maamar, N. Lazaar, S. Loudni, Y. Lebbah. FCP-Miner : Une approche pour la localisation de fautes basée sur l'extraction de motifs ensemblistes sous contraintes. JFPC’2016 - Montpellier - France. [paper] [slides]
- M. Maamar, C. Bessiere, P. Boizumault, N. Lazaar, Y. Lebbah, V. Lemière, S. Loudni. ClosedPattern : Une contrainte globale pour l’extraction de motifs fréquents fermés. JFPC’2017 - Montreuil-sur-Mer - France . [paper] [slides]
Workshops
- M. Maamar, A. Noureddine, N. Lazaar, Y. Lebbah, S. Loudni. FCP-Miner*: A new approach for fault localization using constraint based data-mining. CP meets Verif, CP workshops 2016 - Toulouse - France. [paper] [slides]
maamar[at]cril.fr
+33 6 44 73 55 80
UFR des Sciences Jean Perrin, Rue Jean Souvraz SP 18, 62307 Lens