A hybrid software component clustering and retrieval scheme using an entropy-based fuzzy k-modes algorithm

Modern software development is currently seeking new paths to improve quality and meet time and cost constraints. Reuse of existing software components is considered one of these paths. However, this process experiences significant problems related to efficiently maintaining component repositories, and, moreover, providing the means to discover and retrieve the most suitable ones. This paper aims to provide a methodology to improve the component-based software development process. Specifically, its objective is to introduce an approach that reduces the time to locate suitable software components. The suggested methodology meets the requirements for the efficient searching of components in repositories and also addresses the need for adequate retrieval of the most suitable software components based on the needs of developers. To achieve this we employ a combination of partitional clustering algorithms borrowed from the field of computational intelligence and fuzzy logic thus creating a subset of the available components that are most suitable to the developers' preferences. © 2007 IEEE

Strategies for the intelligent selection of components

It is becoming common to build applications as component-intensive systems - a mixture of fresh code and existing components. For application developers the selection of components to incorporate is key to overall system quality - so they want the `best\u27. For each selection task, the application developer will de ne requirements for the ideal component and use them to select the most suitable one. While many software selection processes exist there is a lack of repeatable, usable, exible, automated processes with tool support. This investigation has focussed on nding and implementing strategies to enhance the selection of software components. The study was built around four research elements, targeting characterisation, process, strategies and evaluation. A Post-positivist methodology was used with the Spiral Development Model structuring the investigation. Data for the study is generated using a range of qualitative and quantitative methods including a survey approach, a range of case studies and quasiexperiments to focus on the speci c tuning of tools and techniques. Evaluation and review are integral to the SDM: a Goal-Question-Metric (GQM)-based approach was applied to every Spiral