IEEE Software’s mission is to be the best source of reliable, useful, peer-reviewed information for leading software practitioners—the developers and managers who want to keep up with rapid technology change. The authority on translating software theory into practice, this bimonthly magazine positions itself between pure research and pure practice, transferring ideas, methods, and experiences among researchers and engineers. Peer-reviewed articles and columns by real-world experts illuminate all aspects of the industry, including process improvement, project management, development tools, software maintenance, web applications and opportunities, testing, and usability.

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Special Issue on The Impact of AI on Productivity and Code
提出日: 2025-08-14

Overview

Is AI truly the key to writing code faster and better? Or do alternative innovations, such as improved user interfaces [8] or other recent breakthroughs in software design [6-7], also play a significant role in enhancing developer productivity and programmer education?

In light of recent advances in AI, there has been no shortage of claims about its ability to transform the developer experience and teaching. The web is filled with promises of vast improvements, often linked to the power of large language models (LLMs) [1-2]. These tools, such as GitHub Copilot and Supermaven, assert they can make coding faster and smarter by automating tasks, enhancing code quality, and streamlining development. For example, the GitHub Copilot website says their tool enables “55% faster coding’,’ while Supermaven’s website claims it enables developers to “write code 2x faster with AI”. Amazon Q Developer’s website says their tool enables “up to 40%” increase in developer productivity.

Moreover, concerns have been raised about whether the speed offered by AI-assisted coding tools may come at the cost of code quality [2-4] and/or comprehension of code. Some studies suggest a “downward pressure on code quality” [2] and security risks [5] when relying heavily on AI-generated code. While LLMs have undoubtedly proven useful in certain areas, the accuracy of AI-generated suggestions often requires scrutiny to avoid introducing bugs or vulnerabilities.

Given these considerations, it is time for a deeper, data-driven investigation. We encourage studies that critically examine the impact of AI on developer productivity, code quality, and developer education. Particularly welcome are industrial case studies or case studies from the classroom that showcase real-world applications of AI tools. We also invite academic researchers to contribute to this discussion.

To move forward, we propose an objective evaluation. Let us search the web for these claims and test their validity through rigorous, evidence-based inquiry. By doing so, we aim to provide a clearer picture for practitioners, researchers, and educators, ensuring that decisions about adopting AI in development are informed by solid, empirical evidence.

Focus

We invite researchers, practitioners, industry experts, and educators to submit original perspectives to explore aspects of developer productivity (or education) that include, but are not limited to the following:

    Industrial perspectives or experience reports (where a one-off case study offers insights into the value, or otherwise, of some AI tool)
    Teaching perspectives or experience reports that comment on the effects of these AI tools on the education experience. Literature reviews of claims made by vendors and of studies testing those claims
    Meta-reviews of prior studies in this area (ideally, analyzing results from multiple prior studies’ data and drawing larger-scale conclusions)
    Critical, unbiased evaluations of tooling (e.g. with GitHub Copilot and other tools)
    Industry perspectives on other hindrances and facilitators of productivity, such as organizational policies, team dynamics, workplace culture, management styles, and remote work and in-office policies
    Proposals for new methods, tooling, or any combination supported by evidence
    Perspectives on how AI tools (including LLMs such as ChatGPT and Claude) impact the education of current students and developers in training. For example, this includes evidence-based notes from faculty on shifting trends in SE education and the role of AI.

Note that any industrial case studies should disclose any conflicts of interest with the AI vendor.

Special Issue on AI Models for Code Improvement
提出日: 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