Semantic and Graph-Based Techniques for Source Code Understanding
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 13
Special Issue Editor
Interests: automated assessment; programming education; e-learning; AI; machine learning; gamification; semantic source code representations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In today's rapidly evolving software landscape, the complexity and scale of codebases pose significant challenges for developers and researchers. Understanding source code is crucial for tasks such as maintenance, debugging, refactoring, and knowledge transfer. However, traditional methods often fail to capture the rich structural relationships and semantic meanings embedded within code. Semantic and graph-based techniques offer richer representations of code structure and meaning, addressing the limitations of traditional approaches. For instance, graph-based methods, such as those using Graph Attention Networks, can improve tasks like code summarization by leveraging both semantic and structural information, while Semantic Code Graphs can facilitate dependency comprehension.
This Special Issue seeks original research contributions that explore the application of semantic and graph-based methods to source code understanding. Topics of interest include, but are not limited to, the following:
- Techniques for capturing and analyzing the meaning of source code, such as semantic code graphs or ontology-driven approaches;
- The development and use of graph-based structures, including Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), Data Flow Graphs (DFGs), and Semantic Code Graphs (SCGs);
- Methods for extracting meaningful information from code to support tasks like code summarization or semantic search;
- Applications of graph neural networks or attention mechanisms for tasks such as code clone detection, defect prediction, and code summarization;
- Combining semantic and graph-based approaches with natural language processing, formal methods, or other methodologies;
- Tools and techniques for visualizing and navigating complex code structures using graphs;
- Evaluations of the effectiveness of these techniques in real-world software engineering scenarios.
We welcome high-quality submissions that offer novel insights, practical tools, or empirical evaluations, and we particularly encourage interdisciplinary approaches that bridge software engineering, machine learning, and programming languages.
Dr. José Carlos Paiva
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- source code understanding
- graph-based techniques
- graph neural networks
- semantic code graphs
- static analysis of source code
- code summarization
- clone detection
- semantic modeling
- abstract syntax trees
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