Infusing Artificial Intelligence into Requirements Quality Assurance

需求捕获被提议的系统所需的特征、功能和属性。如果不加以处理，需求中的缺陷可能会波及整个开发过程，潜在地导致成本超支、构建不良的系统和项目失败。为了尽可能早地减轻和识别需求缺陷，系统的度量对于需求质量保证（RQA）是必要的。在系统和软件工程中，RQA指的是旨在确保系统需求满足所需质量属性的过程和活动，例如完整性和无歧义性。对于复杂的系统，完全手动的RQA方法不仅代价高昂，而且容易出错。因此，对RQA的自动化支持非常重要。

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.