III: Small:Using Data Mining and Recommender Systems to Facilitate Large-Scale Requirements Processes

点击翻译按钮获取中文摘要

Problems related to requirements definitions account for numerous project failures and translate into significant amounts of wasted funds. In many cases, these problems originate from inadequacies in the human-intensive task of eliciting stakeholders' needs, and the subsequent problems of transforming them into a set of clearly articulated and prioritized requirements. These problems are particularly evident in very large projects such as the FBI Virtual Case File or NASA's Space Station, in which knowledge is dispersed across thousands of different stakeholders. On one hand, it is desirable to include as many people as possible in the elicitation and prioritization process, but on the other hand this can quickly lead to a rather chaotic overload of information and opinions. The work proposed under this grant will develop a new framework that utilizes data mining and recommender systems techniques to process and analyze high volumes of unstructured data in order to facilitate large-scale and broadly inclusive requirements processes. The proposal is based on the observation that the requirements elicitation process of many large-scaled industrial and governmental projects is inherently data-driven, and could therefore benefit from computer-supported tools based on data mining and user modeling techniques. INTELLECTUAL MERIT The proposed research will lead to a robust requirements elicitation framework and an associated library of tools which can be used to augment the functionality of wikis, forums, and specialized management tools used in the requirements domain. Specifically, this research will enhance requirements clustering techniques by incorporating prior knowledge and user-derived constraints. A contextualized recommender system will be designed to facilitate appropriate placement of stakeholders into requirements discussion forums generated in the clustering phase. BROADER IMPACT The proposed work has potential for broad impact across organizations that develop stakeholder-intensive systems. Technology transfer can be expected due to collaborations with organizations such as Siemens and Google planned as an integral part of this research. Educational materials will be developed specifically for requirements engineering and recommender systems courses, and will be broadly disseminated. Key Words: Recommender systems; Data mining; Clustering; Requirements engineering; Requirements elicitation.