1 citations · 1 across the 7 of their papers we have counts for
7 papers
Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
Xin-Ye Li, Ren-Biao Liu, Yun-Ji Zhang +3
Reinforcement learning (RL) from unit-test feedback has become a standard post-training recipe for improving large language models (LLMs) on code generation. However, the pass-all-…
ACES: Who Tests the Tests? Leave-One-Out AUC Consistency for Code Generation
Hui Sun, Yun-Ji Zhang, Zheng Xie +4
Selecting LLM-generated code candidates using LLM-generated tests is challenging because the tests themselves may be incorrect. Existing methods either treat all tests equally or r…
Instantiating Standards: Enabling Standard-Driven Text TTP Extraction with Evolvable Memory
Cheng Meng, ZhengWei Jiang, QiuYun Wang +5
Extracting MITRE ATT\&CK Tactics, Techniques, and Procedures (TTPs) from natural language threat reports is crucial yet challenging. Existing methods primarily focus on performance…
Enhancing LLMs in Long Code Translation through Instrumentation and Program State Alignment
Li Xin-Ye, Du Ya-Li, Li Ming
Code translation aims to transform code between programming languages while preserving functionality, with applications in cross-platform development and software migration. Recent…
Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing
Pengyu Xue, Linhao Wu, Zhen Yang +8
In recent years, Large language model-powered Automated Program Repair (LAPR) techniques have achieved state-of-the-art bug-fixing performance and have been pervasively applied and…
Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking
Zhi-Cun Lyu, Xin-Ye Li, Zheng Xie +1
Code generation has been greatly enhanced by the profound advancements in Large Language Models (LLMs) recently. Nevertheless, such LLM-based code generation approaches still strug…
Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond
Pengyu Xue, Linhao Wu, Zhongxing Yu +5
Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs, which facilitate collaboration among developers and play a criti…