8 citations · 10 across the 10 of their papers we have counts for
12 papers
LoLBench: Evaluating Coding Agents with Long-Horizon Proposals on Large Software Systems
Yun Peng, Zihan Wu, Zeyang Zhuang +6
Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks with large reference implementati…
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
Xin Zhou, Chun Yong Chong, Kisub Kim +11
Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs. Yet existing repository-level…
What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agents
Rui Shu, Chun Yong Chong, Xin Zhou +6
Coding agents are ranked almost entirely by resolve rate: whether their final patch passes the target tests. Yet two agents can reach the same outcome through very different proces…
When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation
Guodong Fan, Cuiyun Gao, Chun Yong Chong +4
The rapid evolution of software services poses substantial challenges to the design and implementation of effective recommendation systems. Traditional service recommendation appro…
CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora
Shangyu Li, Juyong Jiang, Meibo Ren +7
Transpilation, or code translation, aims to convert source code from one programming language (PL) to another. It is beneficial for many downstream applications, from modernizing l…
MulVul: Retrieval-augmented Multi-Agent Code Vulnerability Detection via Cross-Model Prompt Evolution
Zihan Wu, Jie Xu, Yun Peng +2
Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability patterns undermines the effectiv…
Benchmarking Code LLM-as-a-Judge via Constrained Perturbation and Calibration
Ruiqi Wang, Xinchen Wang, Cuiyun Gao +3
Code from LLM-assisted development may contain defects like API hallucinations, motivating the development of code evaluation metrics to assess such code before deployment. As conv…
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…
The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation
Shuzheng Gao, Chaozheng Wang, Cuiyun Gao +4
Test cases are essential for validating the reliability and quality of software applications. Recent studies have demonstrated the capability of Large Language Models (LLMs) to gen…
A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How
Chaozheng Wang, Shuzheng Gao, Cuiyun Gao +4
API suggestion is a critical task in modern software development, assisting programmers by predicting and recommending third-party APIs based on the current context. Recent advance…
ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code
Jia Feng, Jiachen Liu, Cuiyun Gao +4
In recent years, the application of large language models (LLMs) to code-related tasks has gained significant attention. However, existing evaluation benchmarks often focus on limi…
CodeLabeller: A Web-based Code Annotation Tool for Java Design Patterns and Summaries
Najam Nazar, Norman Chen, Chun Yong Chong
While constructing supervised learning models, we require labelled examples to build a corpus and train a machine learning model. However, most studies have built the labelled data…