人工智能系统需求工程：系统映射研究
Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study
摘要：
在传统的软件系统中，需求工程（RE）活动已经建立并得到了研究。然而，构建基于人工智能（AI）的软件，而对系统的内部工作机制了解有限或没有了解，这对RE提出了重大的新挑战。现有的文献集中在使用AI来管理RE活动，对RE for AI（RE4AI）的研究有限。[目的]本文研究了当前用于指定人工智能系统需求的方法，确定了用于建模需求的可用框架，方法，工具和技术，并发现了现有的挑战和限制。[方法]我们进行了系统的映射研究，以查找当前RE4AI方法的论文。我们确定了43项主要研究，并分析了现有的方法，模型，工具和技术，用于指定和建模现实世界的场景中的要求。[结果]我们发现了现有RE4AI实践的一些挑战和局限性。研究结果强调，目前的RE应用程序不足以适应构建AI系统，并强调需要提供新的技术和工具来支持RE4AI。[结论]我们的研究结果表明，大多数关于RE4AI的实证研究集中在自动驾驶、自动驾驶车辆和管理数据需求方面，道德、信任和可解释性等领域需要进一步研究。
英文摘要：
[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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
