IEEE Transactions on Software Engineering (TSE) has moved to the OnlinePlus publication model starting with 2013 issues!

From the December 2013 Issue

Supporting Domain Analysis through Mining and Recommending Features from Online Product Listings

By Negar Hariri, Carlos Castro-Herrera, Mehdi Mirakhorli, Jane Cleland-Huang, and Bamshad Mobasher

Featured article thumbnail imageDomain analysis is a labor-intensive task in which related software systems are analyzed to discover their common and variable parts. Many software projects include extensive domain analysis activities, intended to jumpstart the requirements process through identifying potential features. In this paper, we present a recommender system that is designed to reduce the human effort of performing domain analysis. Our approach relies on data mining techniques to discover common features across products as well as relationships among those features. We use a novel incremental diffusive algorithm to extract features from online product descriptions, and then employ association rule mining and the k-nearest neighbor machine learning method to make feature recommendations during the domain analysis process. Our feature mining and feature recommendation algorithms are quantitatively evaluated and the results are presented. Also, the performance of the recommender system is illustrated and evaluated within the context of a case study for an enterprise-level collaborative software suite. The results clearly highlight the benefits and limitations of our approach, as well as the necessary preconditions for its success.

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The IEEE Transactions on Software Engineering (TSE) is an archival journal published bimonthly. We are interested in well-defined theoretical results and empirical studies that have potential impact on the construction, analysis, or management of software.
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