From Prompting to Engineering: A Research Agenda for Prompt Engineering in Software Engineering
Vincenzo De Martino, Giovanna Broccia, Fabiano Pecorelli, Jennifer Horkoff, Riccardo Coppola, Antonino Ferraro, Quim Motger, Emma McKenzie, Shahbaz Siddeeq
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.
Create a lesson
Related papers
ShikumiMiner: Mining Recurring Implementation Patterns in AI Codebases
Afsana Tasnim, Sheikh Motahar Naim
Type Hints in Python Libraries and Frameworks: An Empirical Analysis of Adoption and Maintenance
Thiago Roberto Magalhães, Fabio Petrillo, João Eduardo Montandon
The Import Tax: A Longitudinal Measurement of Startup Cost in the Python Ecosystem
Trinath Sai Subhash Reddy Pittala
Automated Vulnerability Injection in Smart Contracts Using Large Language Models
Luca Migliaccio, Roberto Natella, Naghmeh Ivaki et al.
AgOSS: A Dataset and Multi-Layer Characterization of Open-Source Agricultural Software
Vatsal Dudhaiya, Mikhail Golovenchits, Aryan Banerjee et al.
ExecRetrieval: Measuring the Functional-Correctness Gap in Code-Embedding Retrieval
Aaryan Kapoor, Md Abdullah Al Hafiz Khan