Summary Nowadays, with the development of software reuse, software developers are paying more attention to component‐related technologies, which have been mostly applied in the development of large‐scale complex applications to enhance the productivity of software development and accelerate time to market. Component‐based software development is well acknowledged as a methodology, which establishes the reusability of software and reduces the development cost effectively. Two crucial problems in component‐based software development are component identification and component selection. The main purpose of this paper is to provide a reference point for future research by categorizing and classifying different component identification and component selection methods and emphasizing their respective strengths and weaknesses. We hope that it can help researchers find the current status of this issue and serve as a basis for future activities.
Nowadays, component identification is one of the main challenges of software analysis and design. The component identification process aims at clustering classes into components and subcomponents. There are a number of methods to identify components in the literature; however, most of them cannot be customized to software architect’s preferences. To address this limitation, in this paper, we propose a preference-based method by the name of preference-based component identification using particle swarm optimization (PCI-PSO) to identify logical components. PCI-PSO provides a novel method to handle the software architect’s preferences using an interactive (i.e. human in the loop) search. PCI-PSO employs a customized PSO to automatically classify classes into suitable logical components and avoid the problem of identifying the proper number of components. We evaluated the effectiveness of PCI-PSO with four real-world cases. Results revealed that PCI-PSO has an ability to identify more cohesive and independent components with respect to the software architect’s preferences in comparison to the existing component identification methods.
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