TY - GEN
T1 - Prompt Me
T2 - 31st International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2025
AU - Dąbrowski, Jacek
AU - Bennaceur, Amel
AU - Rajbahadur, Gopi Krishnan
AU - Nuseibeh, Bashar
AU - Alrimawi, Faeq
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - [Context and Motivation] Software engineers can interact with users through digital channels (e.g., online forums) to exchange information about software products and achieve their requirements engineering (RE) goals. However, conducting RE manually is challenging due to the large number of users and the volume of their online feedback. [Question/Problem] Previous work has proposed tools to automatically extract useful information from online feedback (e.g., feature requests); however, these tools suffer from three major limitations: (i) an overlooked RE perspective in their design and evaluation; (ii) insufficient functional and performance capabilities; and (iii) missing evaluations of their ability to address RE needs. [Principal Idea/Results] This paper presents a vision for an intelligent RE software agent designed to overcome these limitations. Specifically, our vision explores how RE can guide the design and evaluation of software agents powered by large language models (LLMs), proposes empirical assessments of LLMs for RE usage and the agent’s ability to meet RE needs. [Contributions] Our contribution is threefold: (i) a vision for an RE agent, (ii) identification of key challenges, and (iii) a roadmap to address current limitations.
AB - [Context and Motivation] Software engineers can interact with users through digital channels (e.g., online forums) to exchange information about software products and achieve their requirements engineering (RE) goals. However, conducting RE manually is challenging due to the large number of users and the volume of their online feedback. [Question/Problem] Previous work has proposed tools to automatically extract useful information from online feedback (e.g., feature requests); however, these tools suffer from three major limitations: (i) an overlooked RE perspective in their design and evaluation; (ii) insufficient functional and performance capabilities; and (iii) missing evaluations of their ability to address RE needs. [Principal Idea/Results] This paper presents a vision for an intelligent RE software agent designed to overcome these limitations. Specifically, our vision explores how RE can guide the design and evaluation of software agents powered by large language models (LLMs), proposes empirical assessments of LLMs for RE usage and the agent’s ability to meet RE needs. [Contributions] Our contribution is threefold: (i) a vision for an RE agent, (ii) identification of key challenges, and (iii) a roadmap to address current limitations.
KW - AI4RE
KW - Artificial Intelligence
KW - Bot
KW - GenAI
KW - Large Language Model
KW - RE4AI
KW - Requirements Engineering
KW - Software Agent
UR - https://www.scopus.com/pages/publications/105002718442
U2 - 10.1007/978-3-031-88531-0_17
DO - 10.1007/978-3-031-88531-0_17
M3 - Conference contribution
AN - SCOPUS:105002718442
SN - 9783031885303
T3 - Lecture Notes in Computer Science
SP - 235
EP - 243
BT - Requirements Engineering
A2 - Hess, Anne
A2 - Susi, Angelo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 April 2025 through 10 April 2025
ER -