{"title":"Artificial intelligence for source code understanding tasks: A systematic mapping study","authors":"Dzikri Rahadian Fudholi, Andrea Capiluppi","doi":"10.1016/j.infsof.2025.107915","DOIUrl":null,"url":null,"abstract":"<div><h3>Context:</h3><div>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.</div></div><div><h3>Objective:</h3><div>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.</div></div><div><h3>Methods:</h3><div>A review of 227 peer-reviewed studies identifies trends and provides a structured mapping addressing each RQ.</div></div><div><h3>Results:</h3><div>The findings reveal a dominant shift toward deep learning, especially transformer-based and graph-based models, highlighting underexplored areas such as explainability.</div></div><div><h3>Conclusion:</h3><div>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.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"189 ","pages":"Article 107915"},"PeriodicalIF":4.3000,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Software Technology","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095058492500254X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal''s scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include:
• Software management, quality and metrics,
• Software processes,
• Software architecture, modelling, specification, design and programming
• Functional and non-functional software requirements
• Software testing and verification & validation
• Empirical studies of all aspects of engineering and managing software development
Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information.
The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.