Year: 2025
Type: conference-paper
Abstract: In software engineering, effective communication is key to ensuring software quality. On GitHub projects, one of the primary channels for such communication is pull request discussions. These discussions often contain high-level design decisions. Understanding communication dynamics within development teams is crucial in software engineering, and sentiments and emotions play a significant role in this context. While there are datasets available for sentiment analysis in this field, those focusing on specific emotions are rare, and some contexts, such as pull request discussions, remain underrepresented. To address these gaps, we propose a novel methodology for capturing sentiments and emotions in contextspecific data, resulting in the creation of PRemo. PRemo includes ≈1.8K manually labeled pull-request messages from 36 active opensource industry-relevant projects. It provides data on individual emotions (and their intensity), the surrounding context, and evaluator confidence. Built using a robust triple validation and two-pass labeling process, the dataset leverages an established psychological emotion model. Already applied in prior research, PRemo is a valuable resource for advancing emotion analysis in software engineering.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G116629194, G1363222504, POSE: Phase II: Expanding the data.table ecosystem for efficient big data manipulation in R, Collaborative Research: Learning Software Engineering by Contributing to Real Projects With Chatbot Assistance, G247810146, G3751890809, G4521367184, A Learning Environment for an Open-Source Contribution Model, G6815844370, EcoSustain: computer and data science for the environment, G6962569482
OpenAlex ID: W4414985249