Recovering Runtime Architecture Models and Managing their Complexity using Dynamic Information and Composite Structures

Soumia Zellagui 1 Chouki Tibermacine 1 Ghizlane Boussaidi 2 Abdelhak-Djamel Seriai 1 Hinde Lilia Bouziane 1 Christophe Dony 1
1 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 ETS - Ecole de Technologie Supérieure [Montréal]
Abstract : Program comprehension during software maintenance is a difficult task, hence the need to support it by recovering the as-built architecture of the system to be maintained. In this paper, we propose a method to recover runtime architecture models of object-oriented systems. The method combines static and dynamic analysis to recover an Object Graph (OG) which includes information that will be used to manage the complexity of this graph. In addition, composition/ownership relations between objects are exploited to embed composite structures into the OG nodes. This enables to support a hierarchical visualization of the recovered architecture. We implemented a prototype of the method and experimented it on a case study. The results of this experimentation are promising.
Keywords : Object oriented architectures Architecture Recovery Static and Dynamic analyses Model checking
Type de document :
Communication dans un congrès
SAC: Symposium on Applied Computing, Apr 2018, Pau, France. ACM Press, 33rd ACM/SIGAPP Symposium On Applied Computing, 2018, 〈https://www.sigapp.org/sac/sac2018/〉. 〈10.1145/3167132.3167420〉
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Contributeur : Tibermacine Chouki <>
Soumis le : jeudi 12 avril 2018 - 10:54:08
Dernière modification le : vendredi 15 février 2019 - 20:46:02

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Soumia Zellagui, Chouki Tibermacine, Ghizlane Boussaidi, Abdelhak-Djamel Seriai, Hinde Lilia Bouziane, et al.. Recovering Runtime Architecture Models and Managing their Complexity using Dynamic Information and Composite Structures. SAC: Symposium on Applied Computing, Apr 2018, Pau, France. ACM Press, 33rd ACM/SIGAPP Symposium On Applied Computing, 2018, 〈https://www.sigapp.org/sac/sac2018/〉. 〈10.1145/3167132.3167420〉. 〈lirmm-01706074〉

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