9 papers
LLM-Assisted Empirical Software Engineering: Systematic Literature Review and Research Agenda
Victoria Gomes, Delaney Selb, Fabio Palomba +2
Context: Empirical Software Engineering (ESE) faces increasing challenges due to data scale, methodological complexity, and reproducibility concerns. Large Language Models (LLMs) h…
Future of Software Engineering Research: The SIGSOFT Perspective
Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik +4
As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusi…
Aligning Academia with Industry: An Empirical Study of Industrial Needs and Academic Capabilities in AI-Driven Software Engineering
Hang Yu, Yuzhou Lai, Li Zhang +6
The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE), driving a paradigm shift in both academic research and industrial practi…
Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings
Alexandra González, Xavier Franch, David Lo +1
Context: Open-source Pre-Trained Models (PTMs) provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software E…
How do Machine Learning Models Change?
Joel Castaño, Rafael Cabañas, Antonio Salmerón +2
The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Fa…
A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
Cuiyun Gao, Guodong Fan, Chun Yong Chong +5
Model hallucination is one of the most critical challenges faced by Large Language Models (LLMs), especially in high-stakes code intelligence tasks. As LLMs become increasingly int…