Requirements elicitation: Towards the unknown unknowns

A. Sutcliffe, P. Sawyer
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引用次数: 173

Abstract

Requirements elicitation research is reviewed using a framework categorising the relative `knowness' of requirements specification and Common Ground discourse theory. The main contribution of this survey is to review requirements elicitation from the perspective of this framework and propose a road map of research to tackle outstanding elicitation problems involving tacit knowledge. Elicitation techniques (interviews, scenarios, prototypes, etc.) are investigated, followed by representations, models and support tools. The survey results suggest that elicitation techniques appear to be relatively mature, although new areas of creative requirements are emerging. Representations and models are also well established although there is potential for more sophisticated modelling of domain knowledge. While model-checking tools continue to become more elaborate, more growth is apparent in NL tools such as text mining and IR which help to categorize and disambiguate requirements. Social collaboration support is a relatively new area that facilitates categorisation, prioritisation and matching collections of requirements for product line versions. A road map for future requirements elicitation research is proposed investigating the prospects for techniques, models and tools in green-field domains where few solutions exist, contrasted with brown-field domains where collections of requirements and products already exist. The paper concludes with remarks on the possibility of elicitation tackling the most difficult question of `unknown unknown' requirements.
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需求引出:朝向未知的未知
需求引出研究使用一个框架对需求规范的相对“认识”和共同基础话语理论进行分类。本研究的主要贡献在于从这一框架的角度回顾需求启发,并提出解决涉及隐性知识的突出启发问题的研究路线图。研究了启发技术(访谈、场景、原型等),然后是表示、模型和支持工具。调查结果表明,尽管创造性要求的新领域正在出现,但启发技术似乎相对成熟。表示和模型也得到了很好的建立,尽管对领域知识进行更复杂的建模仍有可能。虽然模型检查工具继续变得越来越精细,但NL工具(如文本挖掘和IR)的增长也越来越明显,这些工具有助于对需求进行分类和消除歧义。社会协作支持是一个相对较新的领域,它促进了产品线版本的分类、优先级和匹配需求集合。提出了未来需求激发研究的路线图,调查技术、模型和工具在绿色领域的前景,其中存在很少的解决方案,与已经存在需求和产品集合的棕色领域形成对比。论文的结论是对“未知未知”需求这一最困难问题的启发的可能性的评论。
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