Neural Methods for Programming: A Comprehensive Survey and Future Directions

The advancement of neural-based models has driven significant progress in modern code intelligence, accelerating the development of intelligent programming tools such as code assistants and automated software engineering systems. This study presents a comprehensive and systematic survey of neural methods for programming tasks within the broader context of software development. Guided by six research questions, this study synthesizes insights from more than 250 scientific papers, the majority of which were published between 2015 and 2025, with earlier foundational works (dating back to the late 1990s) included for historical context. The analysis spans 18 major programming tasks, including code generation, code translation, code clone detection, code classification, and vulnerability detection. The survey methodologically examines the development and evolution of neural approaches, the datasets employed, and the performance evaluation metrics adopted in this field. It traces the progress in neural techniques from early code modeling approaches to advanced Code-specific Large Language Models (Code LLMs), emphasizing their advantages over traditional rule-based and statistical methods. A taxonomy of evaluation metrics and a categorized summary of datasets and benchmarks reveal both progress and persistent limitations in data coverage and evaluation practices. The review further distinguishes neural models designed for natural language processing and programming languages, highlighting the structural and functional characteristics that influence model performance. Finally, the study discusses emerging trends, unresolved challenges, and potential research directions, underscoring the transformative role of neural-based architectures, particularly Code LLMs, in enhancing programming and software design activities and shaping the future of AI-driven software development.