Open AccessRecommender Systems in Requirements Engineering
Author(s) -
Mobasher Bamshad,
ClelandHuang Jane
Publication year - 2011
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v32i3.2366
Subject(s) - requirements elicitation , requirements management , requirements analysis , requirements engineering , computer science , domain (mathematical analysis) , recommender system , process (computing) , stakeholder , requirement prioritization , non functional requirement , requirement , goal modeling , systems engineering , software engineering , engineering , world wide web , software , software development , programming language , operating system , mathematical analysis , public relations , mathematics , political science , software construction
Requirements engineering in large‐scale 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.