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Special Issue on AI Models for Code Improvement
Submission Date: 2025-10-10

The rise of AI models, including Large Language Models (LLMs), is transforming software engineering by redefining how developers tackle code improvement tasks, such as refactoring and bug detection. Traditionally time-consuming and error-prone, these tasks can now be automated and enhanced through the application of AI. These models are offering unprecedented support, from improving code quality to autonomously detecting and fixing bugs, enabling software teams to focus on higher-level challenges and innovation. Beyond source code analysis, incorporating additional data sources—such as software models, requirements, and issue-tracking documents (e.g., JIRA reports)—can further enrich AI-driven software maintenance, providing deeper insights and more comprehensive support for developers. This special theme aims to explore cutting-edge advancements in the application of AI models to automate and optimize code improvement processes. We welcome contributions that address how these technologies are reshaping software development workflows, discuss their impact on software quality, and share real-world applications and challenges of integrating these tools into development workflows. 

We invite researchers, practitioners, and industry experts to submit their original contributions to IEEE Software Special Theme on AI Models for Code Improvement. This special theme aims to bring together professionals from academia and industry to explore the latest advancements, challenges, and solutions in the use of AI models for code improvement. We welcome papers that cover a wide range of topics, including but not limited to: 

    Bug Detection and Automated Fixing Generation. 
    Comparative Studies of AI Models and Traditional Tools. 
    Intelligent Code Smell Detection. 
    AI-assisted Technical Debt Management. 
    Case Studies and Industrial Applications of AI for Code Improvement.
    AI-driven Adaptive Refactoring. 
    Improving Code Reliability and Security with AI models. 
    Human-AI Collaboration in Refactoring and Debugging.
    Ethical and Practical Considerations in using AI models for code improvement.
    Challenges and limitations of AI models for Code Improvement