Year: 2026
Type: article
Abstract: Accurately estimating story points in Agile Scrum environments remains a challenging task, as traditional models often struggle to capture the complex relationships between user stories and their corresponding effort estimations. In this study, we leverage Gemini’s embedding representations to enhance the modeling of user stories within a story point estimation dataset. To improve prediction performance, we propose GemSP, an ensemble regression model that integrates two complementary regression techniques applied to the Gemini embeddings. Our approach aims to exploit the rich semantic representations of user stories while benefiting from the robustness of ensemble learning. Experimental results show that, when instantiated with Gemini embeddings, the proposed GemSP framework achieves lower prediction error than selected baseline models (GPT-2, Deep-SE, and GPT2SP) under cross-project evaluation on JIRA datasets. These results illustrate the practical benefit of decoupling semantic representation learning from regression, enabling effective integration of stronger embedding models within lightweight ensemble predictors.
Source: Information
DOI: 10.3390/info17010110
Authors: Imad Moufidi, Safaa Achour, Mohammed Benattou
Institution: Université Ibn-Tofail
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7125534523