[Context] In traditional software systems, Requirements Engineering (RE) activities are well-established and researched. However, building Artificial Intelligence (AI) based software with limited or no insight into the system's inner workings poses significant new challenges to RE. Existing literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). [Objective] This paper investigates current approaches for specifying requirements for AI systems, identifies available frameworks, methodologies, tools, and techniques used to model requirements, and finds existing challenges and limitations. [Method] We performed a systematic mapping study to find papers on current RE4AI approaches. We identified 43 primary studies and analysed the existing methodologies, models, tools, and techniques used to specify and model requirements in real-world scenarios. [Results] We found several challenges and limitations of existing RE4AI practices. The findings highlighted that current RE applications were not adequately adaptable for building AI systems and emphasised the need to provide new techniques and tools to support RE4AI. [Conclusion] Our results showed that most of the empirical studies on RE4AI focused on autonomous, self-driving vehicles and managing data requirements, and areas such as ethics, trust, and explainability need further research.
翻译:在传统软件系统中,要求工程(RE)活动是既定的和研究的,然而,建立基于人工智能(AI)的软件,对系统的内部工作了解有限或没有深入了解,对RE提出了新的重大挑战。现有文献侧重于利用AI管理RE活动,对RE(RE4AI)的研究有限。[目标]本文件调查了目前确定AI系统要求的方法,查明了用于模拟要求的现有框架、方法、工具和技术,并发现现有的挑战和限制。[方法]我们进行了系统的绘图研究,以找到关于RE4AI目前方法的文件。我们确定了43项主要研究,并分析了用于在现实世界情景中确定和示范要求的现有方法、模型、工具和技术。[Results]我们发现现有的RE4AI做法存在若干挑战和局限性。研究结果强调,目前的RE应用没有为建立AI系统作充分的调整,并强调有必要提供新的技术和工具来支持RE4AI。[结论]我们的结果表明,关于RE4AI的大多数经验研究都侧重于自主、自我驱动力和数据管理领域。