How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions
University of Stuttgart（斯图加特大学）；University of Kotli Azad Jammu and Kashmir（阿扎德查谟和克什米尔科特利大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Technical University of Munich（慕尼黑工业大学）
Umm-e- Habiba, Markus Haug, Justus Bogner, Stefan Wagner
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Accepted in Requirements Engineering Journal, 2024
Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.
Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
Delft University of Technology（代尔夫特理工大学）；New York University Abu Dhabi（纽约大学阿布扎克分校）；Roskilde University（罗斯基尔德大学）；Universitat Politècnica de Catalunya（巴塞罗那理工大学）；Wageningen University & Research（瓦格宁根大学与研究学院）；University of Tartu（塔尔图大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Twente（代尔夫特理工大学）；University of Minho（明德大学）；University of Groningen（Groningen大学）；Fraunhofer Institute for Experimental Software Engineering（弗劳恩霍夫实验软件工程研究所）；University of L’Aquila（拉奎拉大学）；Universiteit van Amsterdam（阿姆斯特丹大学）
Requirements Practices and Gaps When Engineering Human-Centered Artificial Intelligence Systems
工程人工智能（AI）软件是一个相对较新的领域，有许多挑战，未知数和有限的证明最佳实践。谷歌、微软和苹果等大公司最近提供了一套指导方针，以帮助工程团队构建以人为本的人工智能系统。[目的]实践者目前所采用的开发这样的系统，特别是在需求工程（RE），很少研究和报告的日期。[方法]本文介绍了一项调查的结果，旨在了解当前的RE for AI（RE4AI）行业实践，并确定应遵循哪些关键的以人为本的AI指南。我们的调查是基于映射现有的行业准则，最佳实践和文献中的努力。[结果] 29名专业人士认为，RE中所有以人为本的方面都应该得到重视。此外，我们发现大多数参与者使用UML或Microsoft Office来呈现需求。[结论]我们发现，目前使用的大多数工具都不适合管理基于AI的软件，使用UML和Office可能会对AI捕获的需求质量造成问题。此外，从指南中映射的所有以人为本的实践都应该包括在RE中。
Industry Best Practices in Robotics Software Engineering
Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；Robert Bosch Corporate Research（罗伯特·博世企业研究院）；Waymo；XITASO GmbH（XITASO有限公司）；Mälardalen University（梅拉达伦大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Stuttgart（斯图加特大学）
DUCTILE: Agentic LLM Orchestration of Engineering Analysis in Product Development Practice
Product Development Division, Department of Mechanical Engineering, Chalmers University of Technology, Gothenburg, Sweden（产品开发部门，机械工程系，楚姆勒斯技术大学，哥德堡，瑞典）；GKN Aerospace Engine Systems, Trollhättan, Sweden（GKN航空发动机系统，特罗尔哈塔纳，瑞典）
Naming the Pain in Machine Learning-Enabled Systems Engineering
Pontifical Catholic University of Rio de Janeiro (PUC-Rio)（里约热内卢天主教大学）；Blekinge Institute of Technology (BTH)（布莱金厄理工学院）；University of Bari（巴里大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；Vienna University of Technology (TUW)（维也纳工业大学）；German Aerospace Center (DLR)（德国航空航天中心）；University of Cologne（科隆大学）
From Prompting to Engineering: A Research Agenda for Prompt Engineering in Software Engineering
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；CNR（意大利国家研究委员会）；Pegaso University（佩加索大学）；University of Gothenburg and Chalmers University of Technology（哥德堡大学与查尔姆斯理工大学）；Politecnico di Torino（都灵理工大学）；University of Glasgow（格拉斯哥大学）；Tampere University（坦佩雷大学）
Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education
R. B. Annis School of Engineering（R. B. Annis 工程学院）；University of Indianapolis（印第安纳大学）；E. S. Witchger School of Engineering（E. S. Witchger 工程学院）；Marian University（玛丽安大学）；University of Maryland Eastern Shore（马里兰大学东部分校）；The Boehm Center for Systems and Software Engineering（Boehm 系统与软件工程中心）
