Artificial intelligence for source code understanding tasks: A systematic mapping study
Dzikri Rahadian Fudholi, Andrea Capiluppi · Information and Software Technology · 2025
Context: Artificial intelligence (AI) techniques, particularly natural language processing (NLP) and machine learning (ML), are increasingly used to support source code understanding, an essential activity in software engineering. Objective: This systematic mapping study investigates how these techniques are applied, guided by four Research Questions (RQs) focusing on the types of tasks, embedding methods & preprocessing techniques used, machine learning models employed, and existing research gaps. Methods: A review of 227 peer-reviewed studies identifies trends and provides a structured mapping addressing each RQ. Results: The findings reveal a dominant shift toward deep learning, especially transformer-based and graph-based models, highlighting underexplored areas such as explainability. Conclusion: This study provides a task-based classification and offers insights and directions for future research in AI-enabled source code understanding, supporting both researchers and practitioners.