11 citations · 31 across the 8 of their papers we have counts for
9 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…
FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair
Ruize Ma, Yilei Jiang, Shilin Zhang +7
Multimodal Automated Program Repair (MAPR) extends traditional program repair by requiring models to jointly reason over source code, textual issue descriptions, and visual artifac…
RepoRepair: Leveraging Code Documentation for Repository-Level Automated Program Repair
Zhongqiang Pan, Chuanyi Li, Wenkang Zhong +3
Automated program repair (APR) struggles to scale from isolated functions to full repositories, as it demands a global, task-aware understanding to locate necessary changes. Curren…
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…
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…