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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…
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…
iCodeReviewer: Improving Secure Code Review with Mixture of Prompts
Yun Peng, Kisub Kim, Linghan Meng +1
Code review is an essential process to ensure the quality of software that identifies potential software issues at an early stage of software development. Among all software issues…
COFFE: A Code Efficiency Benchmark for Code Generation
Yun Peng, Jun Wan, Yichen Li +1
Code generation has largely improved development efficiency in the era of large language models (LLMs). With the ability to follow instructions, current LLMs can be prompted to gen…
PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback
Yun Peng, Akhilesh Deepak Gotmare, Michael Lyu +3
Large Language Models (LLMs) are widely adopted for assisting in software development tasks, yet their performance evaluations have narrowly focused on the functional correctness o…