Algorithmic evaluation demonstrates effective software architecture recovery across real-world systems, indicating high fidelity to original system designs.
Software clustering is a technique that is used to manage a software system by partitioning it into smaller subsystems containing highly related modules. Search-based software clustering techniques are found to be beneficial in effective partitioning of software systems. In this paper, we propose a search-based technique that is based on a combination of a nature-inspired black-hole algorithm and a genetic algorithm for software clustering. In order to evaluate the success of this approach, it has been applied to some real-world software systems. It is observed that this approach deduces a structure similar to the original architecture of the software system concerned.
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Jeet et al. (2015) studied this question.
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