A Research Program in Requirements Engineering

需求工程（RE）领域是软件工程领域中最具战略意义的领域之一，因为它体现了定义系统应该提供什么以及系统在使用过程中应该如何表现的方法，技术，工具和过程。如果在系统的需求中有重大的疏忽，那么在系统开发的其余部分投入大量的精力是没有任何意义的。虽然可再生能源在过去几十年中取得了重大进展，但由于不断变化的背景，技术，范式等，该领域存在不断变化的挑战;因此，对可再生能源领域持续改进的追求。 因此，本提案寻求解决可再生能源领域的以下关键挑战： (i)不同应用领域中的数据密集型（或大数据）终端用户应用程序的数量正在稳步增长。然而，在RE中，当前的规范方法并没有明确地捕获这些系统的数据特征。这是有效创建可靠的大数据最终用户应用程序的抑制因素。 (ii)在RE过程中，确保系统符合法规和标准，特别是对于大型、复杂的系统，迄今为止是艰巨的、耗费精力的和容易出错的。目前的可再生能源努力，估计方法的遵守工作是特设的，是不可靠的。这导致了几个问题，即：资源分配，产品质量，可重用性和周期时间。 这项研究是重要的，因为有效的规范方法和努力估计方法将减少所描述的问题显着。反过来，这将有利于大数据和合规系统的用户和开发人员。研究人员也将受益于对可再生能源领域知识体系的贡献。预期的成果包括新的方法：指定大数据最终用户系统的质量要求，并估计在可再生能源中开展监管合规工作所需的工作量。随着知识经济在加拿大企业中的推动力越来越大，拟议的研究成果将有助于宣传这一运动。

The field of requirements engineering (RE) is one of the most strategic fields in the software engineering domain because it embodies methods, techniques, tools and processes for defining what the system should provide and how the system should behave during its use. There isn't any point in investing much effort in the rest of system development if there are major oversights in a system's requirements. While RE has made significant progress over the last several decades, there are evolving challenges in this field due to changing contexts, technologies, paradigms and the like; thus the quest for on-going improvements in the RE field. This proposal therefore seeks to address the following key challenges in the RE field: (i) Data intensive (or Big Data) end-user applications in different application-domains are steadily growing in number. Yet, in RE, current specification methods do not capture, explicitly, data characteristics of such systems. This is an inhibitor for efficiently creating dependable big data end-user applications. (ii) Ensuring system's compliance with regulations and standards, especially for large, complex, systems is hitherto arduous, effort-draining, and error prone, during the RE process. Current RE effort-estimation methods for compliance work are ad hoc and are not reliable. This leads to several problems, namely: resource allocation, product quality, reusability, and cycle-time. This research is important because effective specification methods and effort-estimation methods would reduce the described problems significantly. In turn, this will benefit the users and developers of big data and regulatory compliant systems. Researchers would also benefit because of the contributions to the body of knowledge in the RE field. The anticipated outcomes include novel methods for: specifying quality requirements for big data end-user systems, and estimating the effort needed to carry out regulatory compliance work in RE. With an increasing thrust for knowledge-based economy among Canadian corporations, the outcomes of the proposed research would help in promulgating this movement.