Authors:
Cong Liu
1
;
Boudewijn F. van Dongen
1
;
Nour Assy
1
and
Wil M.P. van der Aalst
2
Affiliations:
1
Eindhoven University of Technology, 5600MB Eindhoven and The Netherlands
;
2
Eindhoven University of Technology, 5600MB Eindhoven, The Netherlands, RWTH Aachen University, 52056 Aachen and Germany
Keyword(s):
Component Identification, Software Execution Data, Community Detection, Empirical Evaluation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Software Development
;
Component-Based Software Engineering
;
Model-Driven Software Development
;
Software Engineering
Abstract:
Restructuring an object-oriented software system into a component-based one allows for a better understanding of the software system and facilitates its future maintenance. A component-based architecture structures a software system in terms of components and interactions where each component refers to a set of classes. In reverse engineering, identifying components is crucial and challenging for recovering the component-based architecture. In this paper, we propose a general framework to facilitate the identification of components from software execution data. This framework is instantiated for various community detection algorithms, e.g., the Newman’s spectral algorithm, Louvain algorithm, and smart local moving algorithm. The proposed framework has been implemented in the open source (Pro)cess (M)ining toolkit ProM. Using a set of software execution data containing around 1.000.000 method calls generated from four real-life software systems, we evaluated the quality of components
identified by different community detection algorithms. The empirical evaluation results demonstrate that our approach can identify components with high quality, and the identified components can be further used to facilitate future software architecture recovery tasks.
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