Emerging Research in Artificial Intelligence for Code Analysis and Security
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 62
Special Issue Editors
Interests: artificial intelligence; source code analysis; NLP
Special Issue Information
Dear Colleagues,
The rise of deep learning has marked the beginning of a new paradigm in artificial intelligence (AI), reshaping the field and opening opportunities across a wide range of disciplines.
In recent years, AI has gained popularity not only within scientific communities but also in everyday applications, largely due to the diffusion of transformer-based generative models. These models, thanks to their remarkable capability in generating human-like natural language and captivating images, have captured public and industry interest alike, accelerating the integration of AI technologies into various domains.
Among these applications, AI’s influence is steadily expanding into software engineering and cybersecurity, where machine learning techniques are being adapted to address some of the traditional problems and challenges. In particular, NLP models originally developed for language understanding are now being applied to source code, while AI-driven techniques are inspiring innovative methodologies for enhancing code quality, detecting vulnerabilities, and broadly supporting various software engineering tasks.
This Special Issue focuses on the convergence of AI and code analysis, highlighting emerging research at the intersection of these fields. From enhancing cybersecurity to optimizing code through machine learning, it explores how AI is transforming the way we understand, analyze, and secure software.
We invite cutting-edge research contributions in cybersecurity and code analysis, with a focus on the transformative impact of AI-driven approaches. Submissions are welcome (but are not limited to) on the following topics:
- AI for automated software engineering (e.g. bug detection, program repair, etc.);
- Code summarization and comment generation;
- AI-driven vulnerability detection;
- AI-driven malware analysis;
- Potential and limitations of LLMs in code analysis;
- Text-based metrics and benchmarks for software;
- Program comprehension;
- NLP for phishing and spam detection;
- Security of machine learning models;
- Adversarial attacks to neural classifiers.
Dr. Martina Saletta
Dr. Claudio Ferretti
Guest Editors
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 1600 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
- artificial intelligence
- cybersecurity
- source code analysis
- software engineering
- code representations
- adversarial attacks
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