Real World Code
Real-world code research focuses on bridging the gap between large language models (LLMs) and practical software development, aiming to improve the quality, security, and efficiency of automatically generated code. Current research emphasizes developing methods for generating equivalent code representations, ensuring code correctness through techniques like hierarchical debugging and polyhedral modeling, and mitigating security vulnerabilities via prompt optimization and generative adversarial networks. This field is significant because it directly impacts software engineering practices, potentially increasing developer productivity and improving software reliability and security.
Papers
From Words to Code: Harnessing Data for Program Synthesis from Natural Language
Anirudh Khatry, Joyce Cahoon, Jordan Henkel, Shaleen Deep, Venkatesh Emani, Avrilia Floratou, Sumit Gulwani, Vu Le, Mohammad Raza, Sherry Shi, Mukul Singh, Ashish Tiwari
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, Lingming Zhang