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.

需求捕获所提出系统的所需特性，功能和属性。如果未解决，需求缺陷可能会在整个开发过程中荡漾，可能导致成本超支，建筑系统不佳和项目故障。为了减轻和识别需求缺陷，需要进行系统的措施，需要进行质量保证（RQA）。在系统和软件工程中，RQA指的是过程和活动旨在确保系统的要求符合所需的质量属性，例如完整性和不合格。对于复杂的系统，对RQA的完全手动方法不仅昂贵，而且是错误的 - 责任。因此，对RQA的自动支持很重要。尽管进行了现有研究，但RQA自动化仍存在重大挑战。在需求文件中无处不在使用自然语言（NL）的第一组挑战。 NL不容易自动化，进一步使歧义检测和完整性检查几乎没有自动支持。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。通过越来越需要遵守标准，法律和法规的系统，假设了第二组挑战。这使得对法律要求的明确处理至关重要，以最大程度地减少不遵守的风险。迄今为止，很少有研究旨在提供自动帮助以确保法律要求的质量。该研究将设计对RQA的新型自动支持，重点是工业环境中的适用性。这项研究的主要假设是，人工智能的最新进展可以大大提高准确性，并减少与某些困难的RQA任务相关的努力。该研究将采用自然语言处理和机器学习的结合，从NL要求中提取结构化信息并对此信息进行分类。这项研究将进一步利用模型驱动的工程，以代表和分析从NL要求中提取的结构化信息，以及对需要检查法律要求质量的法律规定。预计该研究将（1）导致质量保证的大量成本节省，并提高对软件的可靠性 - 密集型系统的信心，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。重要的是，这项研究将为培训具有深入的软件工程专业知识和应用AI专业知识的高素质人员提供理想的背景，并能够满足研究，社会和经济的需求。