11 citations · 32 across the 7 of their papers we have counts for
8 papers
PassSum: Leveraging Paths of Abstract Syntax Trees and Self-Supervision for Code Summarization
Changan Niu, Chuanyi Li, Vincent Ng +3
Code summarization is to provide a high-level comment for a code snippet that typically describes the function and intent of the given code. Recent years have seen the successful a…
On Evaluating the Efficiency of Source Code Generated by LLMs
Changan Niu, Ting Zhang, Chuanyi Li +2
Recent years have seen the remarkable capabilities of large language models (LLMs) for code generation. Different from existing work that evaluate the correctness of the code gener…
FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations
Changan Niu, Chuanyi Li, Vincent Ng +2
While the majority of existing pre-trained models from code learn source code features such as code tokens and abstract syntax trees, there are some other works that focus on learn…
Are We Ready to Embrace Generative AI for Software Q&A?
Bowen Xu, Thanh-Dat Nguyen, Thanh Le-Cong +8
Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Ov…
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models
Changan Niu, Chuanyi Li, Vincent Ng +1
Despite the recent advances showing that a model pre-trained on large-scale source code data is able to gain appreciable generalization capability, it still requires a sizeable amo…
An Empirical Comparison of Pre-Trained Models of Source Code
Changan Niu, Chuanyi Li, Vincent Ng +3
While a large number of pre-trained models of source code have been successfully developed and applied to a variety of software engineering (SE) tasks in recent years, our understa…
Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code
Changan Niu, Chuanyi Li, Bin Luo +1
Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has ena…
SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations
Changan Niu, Chuanyi Li, Vincent Ng +3
Recent years have seen the successful application of large pre-trained models to code representation learning, resulting in substantial improvements on many code-related downstream…