The size and complexity of industrial strength software systems are constantly increasing. This means that the task of managing a large software project is becoming even more challenging, especially in light of high turnover of experienced personnel. Software clustering approaches can help with the task of understanding large, complex software systems by automatically decomposing them into smaller, easier-to-manage subsystems. The main objective of this paper is to identify important research directions in the area of software clustering that require further attention in order to develop more effective and efficient clustering methodologies for software engineering. To that end, we first present the state of the art in software clustering research. We discuss the clustering methods that have received the most attention from the research community and outline their strengths and weaknesses. Our paper describes each phase of a clustering algorithm separately. We also present the most important approaches for evaluating the effectiveness of software clustering.

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SUMMARYSeveral software clustering algorithms have been proposed in the literature, each with its own strengths and weaknesses. Most of these algorithms have been applied to particular software systems with considerable success. However, no algorithm has been shown to be superior in all cases. As a result, selecting a software clustering algorithm that is best suited for a specific software system remains a hard question to answer. At the same time, improving the effectiveness of an existing algorithm is a time‐consuming process that would benefit from a methodology that allowed the early evaluation of an idea. In this paper, we first introduce a formal description template for software clustering algorithms. Based on this template, we propose a novel method for the selection of a software clustering algorithm for specific needs, as well as a method for software clustering algorithm improvement. The applicability and usefulness of the two methods introduced in this paper is demonstrated by applying them in four distinct case studies. Copyright © 2012 John Wiley & Sons, Ltd.

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