8 citations · 12 across the 5 of their papers we have counts for
5 papers · 1 filter
Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
Zhipeng Xue, Xiaoting Zhang, Zhipeng Gao +4
The Large Language Models (LLMs) have demonstrated great potential in code-related tasks. However, most research focuses on improving the output quality of LLMs (e.g., correctness)…
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…
The Current Challenges of Software Engineering in the Era of Large Language Models
Cuiyun Gao, Xing Hu, Shan Gao +2
With the advent of large language models (LLMs) in the artificial intelligence (AI) area, the field of software engineering (SE) has also witnessed a paradigm shift. These models,…
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…