Infusing Artificial Intelligence into Requirements Quality Assurance

将人工智能融入需求质量保证

• 批准号: RGPIN-2020-03991
• 负责人: Sabetzadeh, Mehrdad
• 金额: $ 2.91万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739609
• 关键词: Infusing; Artificial; Intelligence; into; Requirements

Requirements capture the desired characteristics, functions, and properties of a proposed system. If left unaddressed, defects in requirements may ripple through the entire development process, potentially leading to cost overruns, poorly built systems, and project failures. To mitigate against and identify requirements defects as early as possible, systematic measures are necessary for requirements quality assurance (RQA). In systems and software engineering, RQA refers to procedures and activities aiming to ensure that the requirements of a system meet the desired quality attributes, for example, completeness and unambiguity. For complex systems, a fully manual approach to RQA would not only be expensive but also error--prone. Automated support for RQA is thus important.  Despite the existing research, major challenges remain in RQA automation. A first set of challenges relates to the ubiquitous use of natural language (NL) in requirements documents. NL does not lend itself easily to automation, and further, leaves ample room for quality issues to occur. Currently, several key RQA activities for NL requirements, for example, ambiguity detection and completeness checking have little automated support. Similarly, automation is scarce for transforming NL requirements into models that can be used for simulation and testing purposes. A second set of challenges is posed by the fact that systems increasingly have to comply with standards, laws, and regulations. This makes an explicit treatment of legal requirements paramount to minimize the risk of non--compliance. To date, little research has been directed at providing automated assistance for ensuring the quality of legal requirements. The research will devise novel automated support for RQA with an emphasis on applicability in industrial settings. The main hypothesis underlying the research is that recent advances in artificial intelligence can dramatically increase the accuracy and reduce the effort associated with some difficult RQA tasks. The research will employ a combination of natural language processing and machine learning for extracting structured information from NL requirements and classifying this information. The research will further capitalize on model-driven engineering for representing and analyzing the structured information extracted from NL requirements as well as for characterizing the legal provisions against which the quality of legal requirements needs be checked.  The research is expected to (1) lead to major cost savings in quality assurance and increased confidence in the dependability of software--intensive systems, and (2) give the Canadian industry a competitive advantage in developing systems and software quality improvement tools. As importantly, the research will provide an ideal context for training highly qualified personnel with in--depth expertise in software engineering and applied AI, and capable of fulfilling the needs of research, society and the economy.

需求捕获了所提议系统的所需特征、功能和属性，如果不加以解决，需求中的缺陷可能会波及整个开发过程，从而可能导致成本超支、构建不良的系统和项目失败。在系统和软件工程中，RQA 是指旨在确保系统的需求满足所需的质量属性（例如完整性和完整性）的过程和活动。明确。对于复杂的系统，完全手动的 RQA 方法不仅成本高昂，而且容易出错。因此，尽管有现有的研究，RQA 自动化仍然存在主要挑战。自然语言 (NL) 在需求文档中的普遍使用并不容易实现自动化，而且，目前，NL 需求的几个关键 RQA 活动也存在很大的空间。同样，将 NL 需求转换为可用于模拟和测试目的的模型的自动化也很少。系统越来越必须遵守标准、法律和测试，这一事实带来了第二组挑战。这使得对法律要求的明确处理对于最大限度地降低不合规风险至关重要，迄今为止，很少有研究致力于提供自动协助以确保法律要求的质量。强调工业应用该研究的主要假设是，人工智能的最新进展可以显着提高准确性并减少与一些困难的 RQA 任务相关的工作量。该研究将结合自然语言处理和机器学习来从 NL 中提取结构化信息。该研究将进一步利用模型驱动工程来表示和分析从 NL 要求中提取的结构化信息，以及描述需要检查法律要求质量的法律条款。预计将 (1) 节省大量成本质量保证和增强对软件密集型系统可靠性的信心，并且（2）使加拿大行业在开发系统和软件质量改进工具方面具有竞争优势。同样重要的是，该研究将为培训合格的人员提供理想的环境。在软件工程和应用人工智能方面拥有深厚的专业知识，能够满足研究、社会和经济的需求。