Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.
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）
Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering
Department of CSE Chandigarh University Mohali, India（计算机科学与工程系 奇纳格里大学 莫哈利，印度）；Department of CST Tsinghua University Beijing, China（计算机科学与技术系 清华大学 北京，中国）
Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course
Department of Mathematics and Computer Science, University of Bremen（不莱梅大学数学与计算机科学系）；School of Computing Technologies, RMIT University（皇家墨尔本理工学院计算技术学院）
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）
Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry
Chair of Software Engineering, TUM School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院软件工程系）；Business School, Pforzheim University（弗劳恩霍夫大学商学院）；Software and Sustainability (S2) Group, Vrije Universiteit Amsterdam（阿姆斯特丹自由大学软件与可持续性（S2）组）
Teaching Empathy in Software Engineering Education in the Age of Artificial Intelligence
University of Calgary（卡尔加里大学）；Federal Rural University of Pernambuco（伯南布哥联邦农村大学）；University College London（伦敦大学学院）；Radboud University（拉德堡德大学）；University of Otago（奥塔哥大学）；University of Hawaii at Manoa（夏威夷大学马诺阿分校）
On AI Safety and Security Technical Debt in Engineering AI-Enabled Systems
University of Birmingham（伯明翰大学）；HBKU（哈马德·本·哈利法大学）；Bournemouth University（伯恩茅斯大学）；Brunel University London（伦敦布鲁内尔大学）；University of Hawaii（夏威夷大学）；College of Science and Engineering, HBKU（哈马德·本·哈利法大学科学与工程学院）
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）
