Management of Implicit Requirements Data in Large SRS Documents: Taxonomy and Techniques
Management of Implicit Requirements Data in Large SRS Documents: Taxonomy and Techniques
复制标题
大型 SRS 文档中隐式需求数据的管理：分类法和技术
DOI：
10.1145/3552490.3552494
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Anu, Vaibhav
中科院分区：
文献类型：
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作者：
Dave, Dev;Celestino, Angelica;Varde, Aparna S.;Anu, Vaibhav
隐式需求 (IMR) 识别是软件工程中需求工程 (RE) 阶段的一部分，在此阶段收集数据以创建 SRS（软件需求规范）文档。与明确规定的明确要求相反，IMR 构成微妙的数据，需要推断。研究表明，IMR 对于软件开发的成功至关重要。许多软件系统可能会因缺乏 IMR 数据管理而遇到故障。 SRS 文档很大，通常有数百页，因此由软件工程师手动识别 IMR 是不可行的。而且，由于软件系统的扩展，此类数据不断增长。因此，解决 IMR 数据管理的关键问题非常重要。本文对 SRS 文档中的 IMR 进行了调查，包括 IMR 数据的定义和概述、带有解释和示例的 IMR 详细分类、管理 IMR 数据的实践以及 IMR 识别工具。除了回顾经典和最先进的方法之外，我们还强调趋势和挑战，并指出未来研究的开放问题。基于数据质量、隐藏信息检索、准确性和显着性以及从具有复杂异构数据的大型文本文档中发现知识，这篇调查文章很有趣。
Implicit Requirements (IMR) identification is part of the Requirements Engineering (RE) phase in Software Engineering during which data is gathered to create SRS (Software Requirements Specifications) documents. As opposed to explicit requirements clearly stated, IMRs constitute subtle data and need to be inferred. Research has shown that IMRs are crucial to the success of software development. Many software systems can encounter failures due to lack of IMR data management. SRS documents are large, often hundreds of pages, due to which manually identifying IMRs by human software engineers is not feasible. Moreover, such data is evergrowing due to the expansion of software systems. It is thus important to address the crucial issue of IMR data management. This article presents a survey on IMRs in SRS documents with the definition and overview of IMR data, detailed taxonomy of IMRs with explanation and examples, practices in managing IMR data, and tools for IMR identification. In addition to reviewing classical and state-of-the-art approaches, we highlight trends and challenges and point out open issues for future research. This survey article is interesting based on data quality, hidden information retrieval, veracity and salience, and knowledge discovery from large textual documents with complex heterogeneous data.