A recommender system for dynamically evolving online forums
Proceedings of the third ACM conference on Recommender systems
Utilizing recommender systems to support software requirements elicitation
Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering
On-demand feature recommendations derived from mining public product descriptions
Proceedings of the 33rd International Conference on Software Engineering
StakeSource2.0: using social networks of stakeholders to identify and prioritise requirements
Proceedings of the 33rd International Conference on Software Engineering
Recommending source code for use in rapid software prototypes
Proceedings of the 34th International Conference on Software Engineering
Automatic query performance assessment during the retrieval of software artifacts
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
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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.