1 citations · 1 across the 5 of their papers we have counts for
5 papers
Clarity Is Not Assumed: Understanding LLM-Based Code Generation under Ambiguous Requirements
Di Yang, Xinou Xie, Xiuwen Yang +7
Software requirement ambiguity is ubiquitous in real-world development, stemming from the inherent imprecision of natural language and the varying interpretations of stakeholders.…
Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision
Xu Lu, Weisong Sun, Yiran Zhang +4
Automated code generation has long been considered the holy grail of software engineering. The emergence of Large Language Models (LLMs) has catalyzed a revolutionary breakthrough…
Intention is All You Need: Refining Your Code from Your Intention
Qi Guo, Xiaofei Xie, Shangqing Liu +3
Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in siz…
MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation
Yutong Wang, Pengliang Ji, Chaoqun Yang +4
The LLM-as-a-Judge paradigm shows promise for evaluating generative content but lacks reliability in reasoning-intensive scenarios, such as programming. Inspired by recent advances…
NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing
Fei Gao, Ming Hu, Zhiyu Xie +4
With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud Computing (JCC) is promising t…