Exploring Generative Pretrained Transformers to support Sustainability Effect IdentificationPreview Paper
This program is tentative and subject to change.
[Context] Sustainability is increasingly recognized as a critical aspect of software development. [Problem] However, identifying the potential sustainability effects of software systems during Requirements Engineering remains a complex and time-consuming task. [Principal idea] To address this challenge, we explore the use of Generative Pretrained Transformers (GPTs) to automate the generation of these effects across various sustainability dimensions. In this research preview paper, we present our research goals, key research questions, initial findings and next steps. Despite several challenges identified, our tentative conclusion is that GPTs, i.e. ChatGPT, are capable of generating relevant sustainability effects. [Contributions] Our findings aim to contribute to both research and practice by fostering AI-driven approaches for integrating sustainability considerations into requirements engineering.
This program is tentative and subject to change.
Tue 8 AprDisplayed time zone: Brussels, Copenhagen, Madrid, Paris change
16:00 - 17:10 | Research Track - Session R6 - Requirements Elicitation and Analysis IResearch Track at B3 - Teleensenyament | ||
16:00 20mTalk | Automatic Prompt Engineering: the Case of Requirements ClassificationPreview Paper Research Track Mohammad Amin Zadenoori CNR-ISTI, Liping Zhao The University of Manchester, Waad Alhoshan Al-Imam Mohammed Ibn Saud Islamic University, Alessio Ferrari CNR-ISTI | ||
16:20 20mTalk | Exploring Generative Pretrained Transformers to support Sustainability Effect IdentificationPreview Paper Research Track Barbara Paech Heidelberg University, Peter Kaiser University of Applied Science Mannheim, Peter Bambazek Johannes Kepler University Linz, Iris Groher Johannes Kepler University Linz, Norbert Seyff University of Applied Sciences and Arts Northwestern Switzerland FHNW | ||
16:40 30mTalk | ReqRAG: Enhancing Software Release Management through Retrieval-Augmented LLMs: An Industrial StudyEvaluation Paper Research Track Md Saleh Ibtasham Alstom, Sarmad Bashir RISE Research Institutes of Sweden, Muhammad Abbas Khan RISE Research Institutes of Sweden, Zulqarnain Haider Alstom, Mehrdad Saadatmand RISE Research Institutes of Sweden, Antonio Cicchetti Mälardalen University Pre-print |