CONLINE: Complex Code Generation and Refinement with Online Searching
  and Correctness Testing
Xinyi He Jiaru Zou Yun Lin Mengyu Zhou Shi Han Zejian Yuan Dongmei Zhang
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.