Large Language Models (LLMs) have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CONLINE framework, which enhances code generation by incorporating planned online searches for information retrieval and automated correctness testing for iterative refinement. CONLINE also serializes the complex inputs and outputs to improve comprehension and generate test case to ensure the framework's adaptability for real-world applications. CONLINE is validated through rigorous experiments on the DS-1000 and ClassEval datasets. It shows that CONLINE substantially improves the quality of complex code generation, highlighting its potential to enhance the practicality and reliability of LLMs in generating intricate code.

CONLINE框架通过结合计划的在线搜索和自动正确性测试来增强代码生成，提高复杂代码生成的质量，突显其在生成复杂代码中提高实用性和可靠性的潜力。

CONLINE：带在线搜索和正确性测试的复杂代码生成与优化