Many modern software-intensive systems employ artificial intelligence / machine-learning (AI/ML) components and are, thus, inherently data-centric. The behaviour of such systems depends on typically large amounts of data processed at run-time rendering such non-deterministic systems as complex. This complexity growth affects our understanding on needs and practices in Requirements Engineering (RE). There is, however, still little guidance on how to handle requirements for such systems effectively: What are, for example, typical quality requirements classes? What modelling concepts do we rely on or which levels of abstraction do we need to consider? In fact, how to integrate such concepts into approaches for a more traditional RE still needs profound investigations. In this research preview paper, we report on ongoing efforts to establish an artefact-based RE approach for the development of datacentric systems (DCSs). To this end, we sketch a DCS development process with the newly proposed requirements categories and data-centric artefacts and briefly report on an ongoing investigation of current RE challenges in industry developing data-centric systems.
翻译:许多现代软件密集型系统采用人工智能/机器学习(AI/ML)组件,因此,这些系统本身就以数据为中心。这些系统的行为取决于通常在运行时处理的大量数据,使这种非决定性系统变得复杂。这种复杂的增长影响到我们对要求工程(Reps Engineering)中的需求和做法的理解。然而,对于如何有效满足这类系统的需求,仍然没有什么指导:例如,典型的质量要求类别是什么?我们依赖哪些建模概念,或者需要考虑哪些程度的抽象概念?事实上,如何将这些概念纳入更传统的可再生能源系统的方法中,仍然需要深入的调查。在本研究预览文件中,我们报告为建立基于艺术的再设计方法以发展以数据为中心的系统(DCS)而正在作出的努力。为此,我们用新提出的要求类别和以数据为中心的工艺品来勾画DCS的开发过程,并简要报告正在对工业开发以数据为中心的系统对可再生能源的挑战进行的调查。