Domain 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.

We present a recommender system that models and recommends product features for a given domain. Our approach mines product descriptions from publicly available online specifications, utilizes text mining and a novel incremental diffusive clustering algorithm to discover domain-specific features, generates a probabilistic feature model that represents commonalities, variants, and cross-category features, and then uses association rule mining and the k-Nearest-Neighbor machine learning strategy to generate product specific feature recommendations. Our recommender system supports the relatively labor-intensive task of domain analysis, potentially increasing opportunities for re-use, reducing time-to-market, and delivering more competitive software products. The approach is empirically validated against 20 different product categories using thousands of product descriptions mined from a repository of free software applications.

Requirements elicitation has long been recognized as a crucial activity in any software development project. Unfortunately, the traditional elicitation practices do not scale well when applied to larger projects, where knowledge is distributed across numerous geographically dispersed stakeholders. As a result, new distributed requirements elicitation tools have started to surface, such as online forums and wiki pages. In our previous work, we introduced a framework for supporting distributed elicitation by utilizing data mining and machine learning techniques to automatically group stakeholder ideas into forums, and by using recommender system technologies to help promote these forums to potentially interested stakeholders. The framework is designed to create an open and more inclusive environment where points of view, conflicts, interests and tradeoffs are identified as early as possible. In this paper, we present two substantial enhancements to the Recommender System component of this framework, and demonstrate through experiments how they improve the quality of the recommendations.

Recommender systems can be used in online forums to recommend discussion topics to users; however as these forums are characterized by a constant influx of new users and new posts, it is important to consider the performance of the recommender system under a scenario in which the internal composition of the items to be recommended, i.e., discussion threads, and the user preferences are constantly changing. In this paper we describe and evaluate a forum recommender designed to handle the challenges of dynamically evolving internet forums used to gather and discuss feature requests for various software products. In particular, we empirically show that two proposed enhancements to the representations of user profiles will result in improved recommendation effectiveness in dynamic environments.

Requirements related problems, especially those originating from inadequacies in the human-intensive task of eliciting stakeholderspsila needs and desires, have contributed to many failed and challenged software projects. This is especially true for large and complex projects in which requirements knowledge is distributed across thousands of stakeholders. This short paper introduces a new process and related framework that utilizes data mining and recommender technologies to create an open, scalable, and inclusive requirements elicitation process capable of supporting projects with thousands of stakeholders. The approach is illustrated and evaluated using feature requests mined from an open source software product.