9 citations · 12 across the 10 of their papers we have counts for
12 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…
Towards the Generalizability of Leveraging ChatGPT in APR via Self-enhancing: An Empirical Study
Qingyuan Li, Chuanyi Li, Yaopeng Yang +3
Automated Program Repair (APR) increasingly relies on Large Language Models (LLMs). ChatGPT-enhanced APR uses techniques such as self-correction and autonomous agents to improve re…
UNICS: Multilingual Code Search via Unified Pseudocode and Contrastive Transfer Learning
Ye Fan, Jidong Ge, Chuanyi Li +2
While pre-trained models have achieved remarkable success in code search, their multilingual capabilities remain a major hurdle, plagued by data imbalance, cross-lingual semantic i…
An Extensive Replication Study of the ABLoTS Approach for Bug Localization
Feifei Niu, Enshuo Zhang, Christoph Mayr-Dorn +5
Bug localization is the task of recommending source code locations (typically files) that contain the cause of a bug and hence need to be changed to fix the bug. Along these lines,…
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…
StriderSPD: Structure-Guided Joint Representation Learning for Binary Security Patch Detection
Qingyuan Li, Chenchen Yu, Chuanyi Li +4
Vulnerabilities severely threaten software systems, making the timely application of security patches crucial for mitigating attacks. However, software vendors often silently patch…
Empirical Study of Code Large Language Models for Binary Security Patch Detection
Qingyuan Li, Binchang Li, Cuiyun Gao +2
Security patch detection (SPD) is crucial for maintaining software security, as unpatched vulnerabilities can lead to severe security risks. In recent years, numerous learning-base…
CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
Weida Wang, Dongchen Huang, Jiatong Li +32
We introduce CMPhysBench, designed to assess the proficiency of Large Language Models (LLMs) in Condensed Matter Physics, as a novel Benchmark. CMPhysBench is composed of more than…
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…
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks
Wentao Zou, Qi Li, Jidong Ge +4
Pre-trained models (PTMs) have achieved great success in various Software Engineering (SE) downstream tasks following the ``pre-train then fine-tune'' paradigm. As fully fine-tunin…
Practical Program Repair via Preference-based Ensemble Strategy
Wenkang Zhong, Chuanyi Li, Kui Liu +5
To date, over 40 Automated Program Repair (APR) tools have been designed with varying bug-fixing strategies, which have been demonstrated to have complementary performance in terms…
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…