Advance in Natural Language Processing in Code Understanding and Generation: Bridging Human and Machine Programming Gap

Recent advances in Natural Language Processing (NLP) have unlocked many avenues in the automation of code generation, bug detection, and code summarization. It reviews the emerging area of NLP for code and programming languages. Here we discuss key research areas, including semantic code understanding, cross-language code generation, automatic bug detection and repair, and code summarization. We are interested in how transformer-based models like GPT-4 and Codex can be fine-tuned to target specific domain-specific tasks. We have been able to achieve an average accuracy of 92% in the generation of codes while also reducing the time for bug detection by 15%. We would be able to identify through Graph Neural Networks (GNNs) the role they play in improving code structure understanding and which leads to a 20% improvement in semantic understanding over traditional models. In addition, we discuss ethical issues of secure and biased code generation by presenting methodologies that in our experiments threatened to reduce vulnerabilities up to 30%. During our experiments, we measure the performance of accuracy, code quality, as well as error reductions of various programming languages for bug detection tasks.