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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716756
• 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，可能导致成本超支、系统质量不佳和项目失败。为了尽早缓解和识别需求缺陷，需要系统性的需求质量保证（RQA）措施。在系统和软件工程中，RQA 是指旨在确保系统需求满足所需质量属性（例如完整性和明确性）的程序和活动。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，对 RQA 的自动化支持非常重要。 尽管已有研究，RQA 自动化仍面临重大挑战。第一组挑战涉及需求文档中自然语言 (NL) 的普遍使用。 NL 不容易实现自动化，而且还为质量问题的发生留下了充足的空间。目前，针对 NL 需求的几个关键 RQA 活动（例如歧义检测和完整性检查）几乎没有自动化支持。同样，将自然语言处理需求转换为可用于模拟和测试目的的模型的自动化也很缺乏。第二组挑战是系统越来越必须遵守标准、法律和法规。这使得明确处理法律要求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化协助以确保法律要求的质量。 该研究将为 RQA 设计新颖的自动化支持，重点关注工业环境中的适用性。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，从自然语言需求中提取结构化信息并对这些信息进行分类。该研究将进一步利用模型驱动工程来表示和分析从 NL 需求中提取的结构化信息，以及表征需要检查法律需求质量的法律条款。 该研究预计将（1）导致质量保证方面的大量成本节省，并增强对软件密集型系统可靠性的信心，以及（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，该研究将为培养在软件工程和应用人工智能方面拥有深入专业知识并能够满足研究、社会和经济需求的高素质人才提供理想的环境。