Recommender Systems in Requirements Engineering


  • Bamshad Mobasher DePaul University
  • Jane Cleland-Huang DePaul University



Requirements engineering in large-scaled industrial, government, and international projects can be a highly complex process involving thousands, or even hundreds of thousands of potentially distributed stakeholders. The process can result in massive amounts of noisy and semistructured data that must be analyzed and distilled in order to extract useful requirements. As a result, many human intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine learning techniques. In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements elicitation, and decision support across several requirements analysis and prioritization tasks. We also highlight ongoing challenges and opportunities for applying recommender systems in the requirements engineering domain.

Author Biographies

Bamshad Mobasher, DePaul University

Director, Center for Web Intelligence, School of Computing

Jane Cleland-Huang, DePaul University

Association Professor of Software Engineering, School of Computing




How to Cite

Mobasher, B., & Cleland-Huang, J. (2011). Recommender Systems in Requirements Engineering. AI Magazine, 32(3), 81-89.