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.

Technical articles are peer-reviewed carefully to ensure they offer practical and reliable ideas and techniques to readers. The departments cover key concerns of software development: requirements, design, architecture, tools, technologies, empirical evidence, and quality. We also publish insightful lessons-learned articles by people with stories to tell.

Software engineering experts in architecture and design, quality, project management, education, requirements, and many other areas of expertise help guide the selection of what we publish. Our Letters to the Editor, Point-Counterpoint debates, and Sounding Board contributions from thought leaders challenge the status quo and stimulate debate. The magazine has been helping train newcomers to software engineering since 1984, as professors, project managers, and developers pass on articles for their colleagues and students to read.

Scope

IEEE Software welcomes articles describing how software is developed in specific companies, laboratories, and university environments as well as articles describing new tools, current trends, and past projects’ limitations and failures as well as successes. Sample topics include geographically distributed development; software architectures; program and system debugging and testing; the education of software professionals; requirements, design, development, testing, and management methodologies; performance measurement and evaluation; standards; program and system reliability, security, and verification; programming environments; languages and language-related issues; web-based development; usability; and software-related social and legal issues.

Special Issue on AIware in the FM Era
Submission Date: 2025-04-09

Motivation and Scope

“Software for all and by all” is the future of humanity. AIware, i.e., AI-powered software, has the potential to democratize software creation. The definition of software along with many Software Engineering (SE) aspects, processes, tools, platforms, and techniques will need to be either reimagined, reformulated or redesigned, enabling individuals of all backgrounds to participate in its creation with higher reliability and quality. Over the past decade, software has evolved from human-driven Codeware to the first generation of AIware, known as Neuralware, developed by AI experts. Foundation Models (FMs, including Large Language Models or LLMs), ushered in software’s next generation, Promptware, led by domain and prompt experts. However, this Promptware merely scratches the surface of software’s future. We are already witnessing the emergence of the next generation of software, Agentware, in which humans and intelligent agents jointly lead the creation of software. With the advent of brain-like World Models and brain-computer interfaces, we anticipate the arrival of Mindware, representing the 5th generation of software. Agentware and Mindware promise greater autonomy and widespread accessibility, with non-expert individuals, known as Software Makers, offering oversight to autonomous agents. 

The SE community will need to develop fundamentally new approaches and evolve existing ones, so they are suitable for a world in which software creation is within the reach of Software Makers of all levels of SE expertise, as opposed to solely expert developers. We must recognize a shift in where expertise lies in software creation and start making the needed changes in the type of research that is being conducted, the ways that SE is being taught, and the support that is offered to software makers.

A foundation model (FM) is a machine learning model that is trained on broad data such that it can be adapted to a wide range of downstream tasks. We are already witnessing the current popular form of FM (i.e., Large Language Models (LLMs)) evolving to encompass other forms of data beyond text (e.g., images, audio, and video data). Today’s FM-powered software (FMware) has many limitations that hinder our abilities to develop safe, trustworthy, and high-quality software. For example, hallucination, which is the problem of FMs sometimes generating incorrect or purely fictional texts, has already led to issues with the generation of incorrect information in several court cases. Instead of waiting for better FM technologies, innovative engineering solutions are being developed to mitigate or resolve these issues. For instance, recognizing the tendency of LLMs to hallucinate and their limited mathematical capabilities, engineering solutions have been proposed that enable LLMs to invoke legacy codeware for mathematical computations and to ground themselves using legacy data sources. 

This special issue on AIware aims to explore the state of Software and SE in the FM Era. The issue seeks articles that examine the developer productivity, system quality/trustworthiness, SE education, SE platforms, open/inner source collaborations and the whole SE lifecycle (developing, delivering, debugging, evolving, and monitoring complex AIware) in the FM Era.

We invite papers covering any software engineering aspects of AIware in the FM era (a.k.a, AgentWare and PromptWare), which includes but not limited to some of the topics below. Papers must have a practical perspective and emphasize their value in supporting software practitioners and/or documenting current industrial best practices: 

    How would future software look like in the FM era? How to integrate legacy software in future AIware?   
    What changes are needed, if any, to existing programming models (e.g., object-oriented or functional programming) and SE practices (e.g., test-driven development and agile) when developing and maintaining software in the FM era?
    What roles do autonomous agents play in the SE lifecycle of FMware?
    How will requirement engineering processes and practises change in the FMware era?
    How will inner and open source collaboration evolve in the FM era?
    What kind of release engineering practices do we need for FMware? Are LLMOps comprehensive enough to capture the release engineering needs of FMware?
    How do we debug and monitor FMware?
    How should we change SE curriculum, training and mentoring the FM era?
    How to evolve FMs from the perspective FMware makers?
    What are the best practices and automation approaches for Retrieval Augmented Generation (RAG) engineering? 
    Without getting overwhelmed, what does a software engineering manager need to know in the era of AIWare?

Special Issue on The Impact of AI on Productivity and Code
Submission Date: 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
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