Magazine - IEEE Software
Important Dates Submission Due: 8 May 2026 Expected Publication: Jan/Feb 2027Small uncrewed aerial systems (sUAS) are increasingly transforming domains such as disaster response, environmental monitoring, precision agriculture, and infrastructure inspection. As their presence grows and missions become more complex, these systems are evolving from isolated devices into networked, autonomous swarms. This shift introduces unprecedented challenges for software engineering, particularly in designing reliable, adaptable, and safe sUAS platforms capable of operating in dynamic, mission-critical, and ethically sensitive environments.Beyond technical challenges, the growing autonomy of aerial systems raises critical concerns for software engineers and society alike, including dual-use risks, privacy, accountability, sustainability, and regulatory compliance. Addressing these challenges demands rigorous engineering methods that span the full software life cycle, from eliciting requirements and architecting dependable systems, to testing under diverse operational conditions, deploying in critical contexts, and assuring trustworthy runtime behavior under uncertainty.For software practitioners, sUAS represent a unique intersection of embedded, cyber-physical, and AI-enabled systems. This special issue aims to advance the engineering of trustworthy, adaptive, and responsible software for sUAS, inviting contributions that combine scientific insight with practical relevance. We especially welcome papers that demonstrate tangible improvements in software quality, reliability, assurance, and societal impact.We seek submissions addressing both foundational and emerging challenges, emphasizing methods, practices, and case studies that unite research and real-world application to advance the state of practice in this rapidly evolving field.Topics of interest include, but are not limited to: Requirements engineering for sUAS command, control, and mission planning Software architecture for modularity, resilience, and adaptation Verification, validation, and simulation of sUAS flight software Digital twins, runtime monitoring, and adaptive assurance mechanisms Testing and integration in uncertain or adversarial environments Human–machine teaming and operator-in-the-loop control Certification, traceability, and safety assurance for adaptive or ML-enabled flight systems Ethical, legal, and societal implications of autonomous aerial software Responsible…
Submissions Due: 8 May 2026
Magazine - IEEE Software
Important Dates Submission Due: 5 January 2026 Expected Publication: Sept/Oct 2026Agentic engineering is an emerging discipline focused on the design, development, and operation of systems that exhibit goal-directed autonomy, reasoning, and continuous evolution. Foundation models (FMs), such as large language models (LLM), have been accelerating progress in this area across academia and industry.Agentic systems often involve multiple interacting agents, humans, and tools, requiring rigorous system-level engineering to ensure critical qualities like robustness, safety, and observability. A key design challenge in agentic engineering is the growing capability of FMs/LLMs. Developers must decide whether to rely on the FM/LLM or external tools/systems for the same functionality. These decisions can be made at various stages depending on the problem and context: during design time, development time, or event at runtime from a software engineering perspective, and at pre-training time, post-training time, test/inference time, and post-inference time from an AI perspective. Highly autonomous agentic systems also require continuous monitoring, evaluation, observability, intervention, and oversight after deployment, an emerging discipline referred to as AgentOps. Designing this post-deployment environment is also highly complex, with many interdependent design choices.This special issue aims to address these challenges by exploring cutting-edge engineering methods, techniques, tools, and practices for agentic systems. It seeks articles that provide with insights into the design, development, and operation of agentic systems, emphasizing practical applications and real-world experiences.Topics of interests include, but are not limited to: Requirements engineering for agentic systems Architectural design for agentic systems Verification, validation, and testing of agentic systems AgentOps - DevOps for agentic systems Development processes and lifecycle management for agentic systems Evaluation methodologies, tools, and benchmarks for agentic systems Responsible AI and AI safety of agentic systems Agentic systems for software engineering, including requirements, design, coding, testing, deployment, and operations Human-agent interaction, collaboration, and oversight Risk and impact assessment…
Submissions Due: 5 January 2026
Magazine - IEEE Software
Publication: May/June 2026 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…
Submissions Due: 14 August 2025
Magazine - IEEE Software
Publication: July/August 2026 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 Submission Instructions: For author information…
Submissions Due: 24 October 2025
Magazine - IEEE Software
About IEEE Software 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. Scope of Interest 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 geographically distributed development software architectures program and system debugging and testing the education of software professionals requirements design development testing management methodologies performance measurement and evaluation standards program and system reliability security programming environments languages and language-related issues web-based development usability software-related social and legal issues. Submission Instructions: Articles should be no more than 4,200 words, including 250 words for each figure and table. A maximum of 15 references and author biographies are not included in the word count. The abstract should be no more than 150 words and should describe the overall focus of your manuscript. With your submission, provide three actionable insights in bullet-list format that software practitioners will get from your paper. Please include a photo of each author. To gauge the suitability of an article that you are planning to submit to IEEE Software, you may send your abstract to the editor in chief at sigrid.eldh@ieee.org Before submitting, please read our author guidelines. When you are ready to submit, please go to https://ieee.atyponrex.com/journal/sw-cs. In addition to…
Magazine - IEEE Software
Important Dates Submissions Due: 9 April 2025 *Expected* Publication Issue: Jan/Feb 2026 Call for Papers 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…
Submissions Due: 9 April 2025
Magazine - IEEE Software
Publication: March/April 2026 The energy footprint of software, software engineering, and software-intensive systems poses a significant concern. Energy-hungry software-intensive systems, such as blockchain applications and cryptocurrencies, the pervasive integration and usage of central cloud and edge services and applications, along with AI-enabled systems, contribute to this issue. In addition, the global digital transformation of all industry sectors is accelerating the steep increase in software energy demands. Green clean software pertains to the minimization of the energy needed to execute and use software-intensive systems. Adopting renewable energy resources to “feed” software execution is simply not enough, so reducing the carbon footprint must go hand in hand with minimizing the energy footprint. The other way around, software-intensive systems may be used to support green processes that aim at reducing the environmental impact on the sector, society, and planet Earth. Examples include software supporting the production and consumption of renewable energy resources, smart software for green-oriented behavioral change (e.g., adopting green public transportation and sustainable work practices), and the combination of energy optimization and digitalization (so-called twin transition). In addition, software sustainability from an environmental perspective may also concern software engineering and its processes: the energy used to develop, evolve, and maintain software-intensive systems is non-negligible and needs to be addressed. This IEEE Software Special Theme issue aims to target both the green clean software, and the green through software dimensions, with special emphasis on the role played by green software engineering. Possible topics: Practices and tactics for green clean software sustainability Green AI, AI for green Sustainability in data centers and high-performance computing Digital sufficiency Tradeoffs and balancing ecologic and technical software qualities Green clean software quality assessment Software sustainability by design Architecting for environmental sustainability Green quality metrics for software products and software engineering processes Standards, labels, indicators, and metrics for…
Submissions Due: 12 June 2025
Magazine - IEEE Software
Important Dates Submissions: 15 September 2024 Publication: May/June 2025 The provocative question we are interested in is as follows: Can we really ask a Computer to test software systems without human intervention? Our hypothesis is that AI can perform a whole series of tasks such as design, construction, run and maintain automated test suites and in some cases replace the human being to improve Software Testers' life. In this special issue, we want to collect scientific and industrial works aimed at investigating the synergy between AI and software testing and how AI is reshaping Test Automation. In particular, the main goal is to better understand this still unexplored phenomenon and collect the innovative solutions proposed by AI and how these are put into practice in the available testing tools/frameworks. We invite article submissions covering all aspects of the synergy between Artificial intelligence (AI), machine learning (ML), and software testing and how AI is reshaping test automation, including, but not limited to: AI-powered testing tools and frameworks, and general support for test automation Novel AI based solutions and limitations of traditional automated testing approaches Usage of Large Language Models (e.g., ChatGPT) in software testing Test case and test script generation based on AI Machine Learning and Artificial Intelligence applied to test automation Automated generation of test oracles Test execution automation Quality aspects of using AI for Test automation (e.g., to improve APFD metric and coverage) Testing in an Agile and CI contexts, and testing within DevOps Analytics, learning, and big data in relation to test automation Metrics, benchmarks, and estimation on any type of AI-powered Test Automation Maintainability, monitoring, and refactoring of automated AI-based test suites AI-powered Test Automation patterns Test automation maturity and experience reports on AI-powered Test Automation Evolution of automated AI-based test suites Submission Guidelines Manuscripts must not…
Submissions Due: 15 September 2024
