2 citations · 3 across the 9 of their papers we have counts for
10 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As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers.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…
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…
LLM-as-a-Judge for Software Engineering: Literature Review, Vision, and the Road Ahead
Junda He, Jieke Shi, Terry Yue Zhuo +5
The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks like code generation, producing a massive volume of software artifacts…
CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency
Renyi Zhong, Yintong Huo, Wenwei Gu +6
Comments within code serve as a crucial foundation for software documentation, facilitating developers to communicate and understand the code effectively. However, code-comment inc…
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…
Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks
Yixin Cao, Shibo Hong, Xinze Li +24
Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, th…
Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
Alexandra González, Xavier Franch, David Lo +1
Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailor…
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…