Using Data Mining and Recommender Systems to Facilitate Large-Scale, Open, and Inclusive Requirements Elicitation Processes

Carlos Castro-Herrera, C. Duan, J. Cleland-Huang, B. Mobasher
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引用次数: 57

Abstract

Requirements related problems, especially those originating from inadequacies in the human-intensive task of eliciting stakeholderspsila needs and desires, have contributed to many failed and challenged software projects. This is especially true for large and complex projects in which requirements knowledge is distributed across thousands of stakeholders. This short paper introduces a new process and related framework that utilizes data mining and recommender technologies to create an open, scalable, and inclusive requirements elicitation process capable of supporting projects with thousands of stakeholders. The approach is illustrated and evaluated using feature requests mined from an open source software product.
使用数据挖掘和推荐系统促进大规模、开放和包容的需求引出过程
与需求相关的问题,特别是那些源自于引起涉众需求和愿望的人力密集型任务的不足,导致了许多失败和挑战的软件项目。对于需求知识分布在数千个涉众中的大型和复杂项目来说,这尤其正确。这篇短文介绍了一个新的过程和相关框架,它利用数据挖掘和推荐技术来创建一个开放的、可扩展的、包容的需求激发过程,能够支持有数千个涉众的项目。使用从开源软件产品中挖掘的特性请求来说明和评估该方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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