18 citations · 23 across the 9 of their papers we have counts for
9 papers
EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering
Chenhui Mao, Yuanting Lei, Zhixiang Wei +6
Agentic Test-Time Scaling (TTS) has delivered state-of-the-art (SOTA) performance on complex software engineering tasks such as code generation and bug fixing. However, its practic…
Learning Adaptive Parallel Execution for Efficient Code Localization
Ke Xu, Siyang Xiao, Ming Liang +6
Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate…
PEACE: Towards Efficient Project-Level Efficiency Optimization via Hybrid Code Editing
Xiaoxue Ren, Jun Wan, Yun Peng +5
Large Language Models (LLMs) have demonstrated significant capability in code generation, but their potential in code efficiency optimization remains underexplored. Previous LLM-ba…
NES: An Instruction-Free, Low-Latency Next Edit Suggestion Framework Powered by Learned Historical Editing Trajectories
Xinfang Chen, Siyang Xiao, Xianying Zhu +6
Code editing is a frequent yet cognitively demanding task in software development. Existing AI-powered tools often disrupt developer flow by requiring explicit natural language ins…
Enhancing Project-Specific Code Completion by Inferring Internal API Information
Le Deng, Xiaoxue Ren, Chao Ni +3
Project-specific code completion is a critical task that leverages context from a project to generate accurate code. State-of-the-art methods use retrieval-augmented generation (RA…
FlexFL: Flexible and Effective Fault Localization with Open-Source Large Language Models
Chuyang Xu, Zhongxin Liu, Xiaoxue Ren +3
Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated…
REPOFUSE: Repository-Level Code Completion with Fused Dual Context
Ming Liang, Xiaoheng Xie, Gehao Zhang +6
The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from…
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Bingchang Liu, Chaoyu Chen, Cong Liao +9
Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previo…
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Peng Di, Jianguo Li, Hang Yu +35
Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the…