From Prompting to Engineering: A Research Agenda for Prompt Engineering in Software Engineering
- URL: http://arxiv.org/abs/2609.02248v1
- Date: Wed, 02 Sep 2026 07:55:20 GMT
- Title: From Prompting to Engineering: A Research Agenda for Prompt Engineering in Software Engineering
- Abstract summary: We organized a community discussion at the First International Workshop on Empirical Prompt Engineering for Software Engineering (PROMPT-SE)<n>Participants discussed current prompting practices, challenges to their adoption and evaluation, and future directions for integrating prompt engineering into software development.<n>Based on these areas, we outline a research agenda to move prompt engineering from predominantly ad hoc interactions toward more systematic, maintainable, evaluable, traceable, and governable SE practices.
- Score: 5.3897886920613685
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Prompt engineering is increasingly used across Software Engineering (SE) activities, including requirements analysis, coding, testing, documentation, repository analysis, and planning. Yet prompts and related instruction artifacts are often created and evolved through task-specific and informal practices, with limited support for their systematic evaluation, management, traceability, and governance. To examine how SE can contribute to the maturation of these practices, we organized a structured community discussion at the First International Workshop on Empirical Prompt Engineering for Software Engineering (PROMPT-SE), co-located with EASE 2026. Participants discussed current prompting practices, challenges to their adoption and evaluation, and future directions for integrating prompt engineering into software development. We synthesized these discussions into five areas: prompt artifacts and standardization; evaluation and benchmarking; lifecycle integration; human-AI collaboration and skills; and governance, privacy, and technical debt. Based on these areas, we outline a research agenda to move prompt engineering from predominantly ad hoc interactions toward more systematic, maintainable, evaluable, traceable, and governable SE practices.
Related papers
- Teaching Machine Learning to Software Engineers [1.217503190366097]
Machine learning (ML) and Artificial Intelligence (AI) components are increasingly embedded in software products.<n>Software engineering curricula rarely provide systematic preparation for building, testing, deploying, and maintaining AI/ML-based software systems.<n>This paper aims to provide evidence-based guidance for integrating AI/MLrelevant content into core SE education.
arXiv Detail & Related papers (2026-06-12T16:42:34Z) - Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents [63.03252293761656]
This paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on large language models (LLMs)<n>We examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use.<n>We provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation.
arXiv Detail & Related papers (2025-10-20T12:46:55Z) - Embedding Sustainability in Software Engineering Curriculum: A Case Study [5.37133760455631]
This paper reports on a case study that examines how sustainability is being embedded across modules in the Software Engineering program at one university.<n>The paper outlines the process through which academics and students co-identified opportunities for integration, guided by the five dimensions of the Sustainability Awareness Framework.<n>We argue that such integration is a necessary and urgent step to prepare Software Engineering graduates as sustainability-aware professionals in our changing society.
arXiv Detail & Related papers (2025-10-01T09:06:13Z) - Prompts as Software Engineering Artifacts: A Research Agenda and Preliminary Findings [39.35547205954143]
This research programme characterizes current prompt practices, challenges, and influencing factors in software engineering.<n>We conducted an exploratory survey with 74 software professionals from six countries to investigate current prompt practices and challenges.<n>The findings reveal that prompt usage in SE is largely ad-hoc: prompts are often refined through trial-and-error, rarely reused, and shaped more by individual practitioners than standardized practices.
arXiv Detail & Related papers (2025-09-22T09:08:29Z) - Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices [70.24403396375277]
The "Greening AI with Software Engineering" CECAM-Lorentz workshop was held February 3-7, 2025 in Lausanne, Switzerland.<n>This report presents a research agenda emerging from the workshop.<n>It outlines open research directions and practical recommendations to guide the development of environmentally sustainable AI-enabled systems.
arXiv Detail & Related papers (2025-06-02T15:19:49Z) - A Survey on (M)LLM-Based GUI Agents [62.57899977018417]
Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction.<n>Recent advances in large language models and multimodal learning have revolutionized GUI automation across desktop, mobile, and web platforms.<n>This survey identifies key technical challenges, including accurate element localization, effective knowledge retrieval, long-horizon planning, and safety-aware execution control.
arXiv Detail & Related papers (2025-03-27T17:58:31Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Software engineering in start-up companies: An analysis of 88 experience
reports [3.944126365759018]
This study investigates how software engineering is applied in start-up context.
We identify the most frequently reported software engineering (requirements engineering, software design and quality) and business aspect (vision and strategy development) knowledge areas.
We conclude that most engineering challenges in start-ups stem from inadequacies in requirements engineering.
arXiv Detail & Related papers (2023-11-20T19:42:37Z) - Towards Automated Process Planning and Mining [77.34726150561087]
We present a research project in which researchers from the AI and BPM field work jointly together.
We discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models.
arXiv Detail & Related papers (2022-08-18T16:41:22Z) - Software engineering for artificial intelligence and machine learning
software: A systematic literature review [6.681725960709127]
This study aims to investigate how software engineering has been applied in the development of AI/ML systems.
Main challenges faced by professionals are in areas of testing, AI software quality, and data management.
arXiv Detail & Related papers (2020-11-07T11:06:28Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.