using data mining and recommender systems to scale up the requirements process
Ultra-Large-Scale (ULS) software projects are anticipated to be highly complex and to involve thousands, or even hundreds of thousands of stakeholders. Unfortunately numerous accounts of recent failures and challenges in industrial and governmental projects have demonstrated that current requirements elicitation and prioritization practices do not scale adequately to address the needs of large projects. This position paper directly addresses this problem through proposing an open, inclusive, and robust elicitation and prioritization process that utilizes data-mining and recommender technologies to facilitate the active involvement of many thousands of stakeholders. We believe that the approach described in this paper is a fundamental building block towards addressing higher level requirements problems facing ULS Systems.
data mining recommender systems requirements elicitation requirements prioritization ultra-large scale systems clustering