For JSS's full CfP including information on Special Issues, Industry, Trends, and Journal First tracks please continue to read for further details.

The Journal of Systems and Software publishes papers covering all aspects of software engineering. All articles should provide evidence to support their claims, e.g. through empirical studies, simulation, formal proofs or other types of validation. Topics of interest include, but are not limited to:

    Methods and tools for software requirements, design, architecture, verification and validation, testing, maintenance and evolution
    Agile, model-driven, service-oriented, open source and global software development
    Approaches for cloud/fog/edge computing and virtualized systems
    Human factors and management concerns of software development
    Artificial Intelligence, data analytics and big data applied in software engineering
    Metrics and evaluation of software development resources
    DevOps, continuous integration, build and test automation
    Business and economic aspects of software development processes
    Software Engineering education
    Ethical/societal aspects of Software Engineering
    Software Engineering for AI systems
    Software Engineering for Sustainability
    Methods and tools for empirical software engineering research 

The journal welcomes reports of practical experience for all of these topics, as well as replication studies and studies with negative results. The journal appreciates the submission of systematic literature reviews, mapping studies and meta-analyses. However, these should report interesting and important results, rather than merely providing statistics on publication year, venue etc.

JSS supports Open Science and reproducible research. Therefore, authors are encouraged to make Open Science material available at the time of submission and after acceptance of their manuscript, e.g., by submitting artifacts related to a study to an archived open repository (such as arXiv.org, zenodo.org, Mendeley, etc.). Also, authors are encouraged to explicitly reference Open Science material in their manuscript (e.g., via a DOI from the open repository). If authors are not able to disclose any material (for example, industrial data subject to non-disclosure agreements), we encourage authors to explicitly acknowledge this by including a short statement in their manuscript. Depending on the type of research presented in a manuscript, Open Science material could include study protocols, (anonymized) raw or analyzed data, data analysis scripts, source code, customized tools and infrastructures, experimental material, codebooks, etc. If authors agree to participate in the JSS Open Science Initiative, after the acceptance of a manuscript, they will be invited to submit a link to Open Science material for review by the JSS Open Science Board. After a successful review (which does not impact the acceptance of the manuscript) considering availability and usability of the material, the publisher will add a statement to the final version of the manuscript acknowledging that the Open Science package was validated by the JSS Open Science Board.

In addition to regular papers, JSS features two special tracks (In Practice, New Ideas and Trends Papers), as well as special issues.

In Practice is exclusively focused on work that increases knowledge transfer from industry to research. It accepts: (1) Applied Research Reports where we invite submissions that report results (positive or negative) concerning the experience of applying/evaluating systems and software technologies (methods, techniques and tools) in real industrial settings. These comprise empirical studies conducted in industry (e.g., action research, case studies) or experience reports that may help understanding situations in which technologies really work and their impact. Submissions should include information on the industrial setting, provide motivation, explain the events leading to the outcomes, including the challenges faced, summarize the outcomes, and conclude with lessons learned, take-away messages, and practical advice based on the described experience. Contributing authors from industry are encouraged but not mandatory. (2) Practitioner Insights where we invite experience reports showing what actually happens in practical settings, illustrating the challenges (and pain) that practitioners face, and presenting lessons learned. Problem descriptions with significant details on the context, underlying causes and symptoms, and technical and organizational impact are also welcome. Practitioner insights papers may also comprise invited opinionated views on the evolution of chosen topic areas in practice. In contrast to applied research reports, practitioner insights are limited to four pages and the first author must be from industry. Finally, submissions to this track should be within scope of the journal's above topics of interest and they will be evaluated through industry-appropriate criteria for their merit in reporting useful industrial experience rather than in terms of academic novelty of research results.

New Ideas and Trends Papers

New ideas, especially those related to new research trends, emerge quickly. To accommodate timely dissemination thereof, JSS introduces the New Ideas and Trends Paper (NITP). NITPs should focus on the systems/software engineering aspects of new emerging areas, including: the internet of things, big data, cloud computing, software ecosystems, cyber-physical systems, green/sustainable systems, continuous software engineering, crowdsourcing, and the like. We distinguish two types of NITPs:

    A short paper that discusses a single contribution to a specific new trend or a new idea.
    A long paper that provides a survey of a specific trend, as well as a (possibly speculative) outline of a solution.

NITPs are not required to be fully validated, but preliminary results that endorse the merit of the proposed ideas are welcomed.

We anticipate revisiting specific new trends periodically, for instance through reflection or progress reports.
New Ideas and Trend Papers warrant speedy publication.

Special Issue proposals
To submit a proposal for a special issue please submit your proposal here to Special Issues Editors Prof. Raffaela Mirandola and Prof. Laurence Duchien. Please visit the special issue guidelines page first to review the proposal guidelines and to download the proposal template required when submitting a proposal.

Journal First Initiative

Authors of JSS accepted papers have the opportunity to present their work in those conferences that offer a Journal First track. Using this track, researchers may take the best from two worlds: ensuring high quality in the JSS publication (thorough, multi-phase review process of a long manuscript), while getting feedback from a community of experts and fostering possible collaborations during a scientific event.

Details may vary from conference to conference, but generally speaking, JSS papers to be presented in a Journal First track must report completely new research results or present novel contributions that significantly extend previous work. The ultimate decision to include a paper in the conference program is up to the conference chairs, not JSS. A JSS paper may be presented only once through a Journal First track.

As of today, the list of conferences with which JSS is collaborating, or has collaborated, through a Journal First track, is: ASE, ICSME, SANER, RE, ESEM, PROFES, and APSEC.

Special Issue on Managing Technical Debt in Software-intensive Products and Services
Submission Date: 2024-08-31

Technical Debt “is a collection of design or implementation constructs that are expedient in the short term, but set up a technical context that can make future changes more costly or impossible.” Technical Debt presents an actual or contingent liability whose impact is limited to internal system qualities, primarily maintainability and evolvability.

Technical Debt is one of the most important concepts in software evolution. It expresses development shortcuts taken for expediency but causing the degradation of internal software quality. One of the key aspects of the technical debt metaphor is its nature toward closing the communication gap between technical and non-technical stakeholders in software development teams.

Guest editors:

Dr. Zadia Codabux, University of Saskatchewan, Canada 
Dr. Rodrigo Spinola, Virginia Commonwealth University, USA  
Dr. Carolyn Seaman, University of Maryland Baltimore County, USA  
Dr. Matthias Galster, University of Canterbury, New Zealand 

Special issue information:

This Special Issue solicits papers on Technical Debt in particular (but not exclusively) in the context of large-scale and "intelligent" systems, e.g., systems that offer features based on Big Data, Machine Learning or Artificial Intelligence, and Generative AI. Papers should cover Technical Debt in any area of software engineering, including conceptualizing, defining, identifying, quantifying, reasoning about, prioritizing and remediating, and representing Technical Debt. Articles should consider practical applications, case studies, experiments, or systematic comparisons with other approaches already in practice. Furthermore, the Special Issue is interested in theoretical contributions and significant empirical validation of the claimed contributions. Ideally, papers should offer practical and reliable insights that have been derived from, or that can be applied to real-world software-intensive systems.

The topics of interest include, but are not limited to:

    Quality assurance activities towards Technical Debt management
    Use of artificial intelligence approaches to support Technical Debt Management
    Human factors and Technical Debt
    Interplay between Software Economics and Technical Debt
    Software visualization for supporting Technical Debt identification and monitoring
    Case studies on (un)successful debt management
    Case studies on (un)successful remediation of Technical Debt
    Economic models for Technical Debt, e.g., estimation of principal and interest and related frameworks
    Stakeholders’ concerns on technical, process, and social aspects of Technical Debt
    Measurement frameworks for studying and monitoring different aspects of Technical Debt
    Methods and tools for studying different causes of Technical Debt and empirical evidence on their use (technical, social, process)
    Decision frameworks for prioritizing debt items among themselves and against features
    Architecture evolution, rework, refactoring, and managing Technical Debt
    Requirement, Test, Documentation, Infrastructure, Security, and their relationship to Technical Debt
    Technical Debt and emerging technologies, e.g., Machine Learning, Generative AI, different classes of systems, etc.)
    Assessing Technical Debt in the software development ecosystem (e.g., relationship to DevOps, testing, deployment)
    Replication studies on Technical Debt.

We welcome articles presenting novel and strong contributions to managing Technical Debt, including state-of-the-art methods, models, and tools (with evidence of use and study of practical impact) or bridging the gap between practice and research; empirical studies in the field, addressing one or many human, technical, social, and economic issues of managing Technical Debt through qualitative and/or quantitative analyses; and industrial experiences, including good practices and lessons learned from managing Technical Debt in specific contexts or domains.

Furthermore, in the tradition of JSS we also encourage In Practice papers, which are exclusively focused on work that increases knowledge transfer from industry to research.

Important

    Literature surveys, systematic literature reviews, and mapping studies are out of the scope of this special issue and will be desk-rejected.
    All submissions must explicitly include an empirical validation such as reporting on controlled experiments or case studies or comparing to other approaches already in practice in a comprehensive and head-to-head manner.

Manuscript submission information:

Authors are encouraged to submit high-quality, original work that has neither appeared in nor is under consideration by other journals or conferences. If an earlier version of the work has been published elsewhere, the contribution of this submission must be at least 30% different and the authors should clearly explain in the introduction the delta between this new paper and the prior version. Please note that the papers from TechDebt 2024 invited for this special issue are also subject to the same rule. Authors are requested to attach to their JSS submission their relevant, previously published articles and a summary document explaining the enhancements made in the journal version.

All manuscripts and supplementary material should be submitted through the Elsevier Editorial System at https://www.editorialmanager.com/jssoftware/default2.aspx . During the submission process, please select “VSI: TechDebt” for the "Article Type."

Submissions must be prepared according to the Guide for Authors, available on the journal website. The submitted paper must follow the format specified in the Guide for Authors: https://www.elsevier.com/journals/journal-of-systems-and-software/0164-1212/guide-for-authors.

Note that submissions can only go through at most two revisions, of which the second one can only be a minor revision. No submission is allowed to receive two rounds of major revisions.

A decision on each submission is not only made by the guest editors, but also by the Editors-in-Chief and the Special Issues Editors.

Important Dates:

• Manuscript Submission Deadline: August 31, 2024
• Date first review round completed: October 31, 2024
• Date revised manuscripts due: January 15, 2025
• Date completion of the review and revision process (final 

Special Issue on Automated Testing and Analysis for Dependable AI-enabled Software and Systems
Submission Date: 2024-08-31

Guest editors:​

Matteo Camilli, Politecnico di Milano, Italy
Michael Felderer, German Aerospace Center (DLR) and University of Cologne, Cologne, Germany
Alessandro Marchetto, University of Trento, Italy
Andrea Stocco, Technical University of Munich (TUM) and fortiss GmbH, Germany

Special Issues Editors

Laurence Duchien and Raffaela Mirandola

Editors in Chief

Paris Avgeriou and David Shepherd

Special issue information:

The advancements in Artificial Intelligence (AI) and its integration into various domains have led to the development of AI-enabled software and systems that offer unprecedented capabilities. Technologies ranging from computer vision to natural language processing, from speech recognition to recommender systems enhance modern software and systems with the aim of providing innovative services, as well as rich and customized experiences to the users. Such technologies are also changing the software and system engineering and development methods and tools, especially quality assurance methods that require deep restructuring due to the inherent differences between AI and traditional software.

AI-enabled software and systems are often large-scale driven by data, and more complex than traditional software and systems. They are typically heterogeneous, autonomous, and probabilistic in nature. They also lack of transparent understanding of their internal mechanics. Furthermore, they are typically optimized and trained for specific tasks and, as such, may fail to generalize their knowledge to other situations that often emerge in dynamic environments. These systems strongly demand safety, trustworthiness, security, and other dependability aspects. High-quality data and AI components shall be safely integrated, verified, maintained, and evolved. In fact, the potential impact of a failure, or a service interruption, cannot be tolerated in business-critical applications (e.g., chatbots and virtual assistants, facial recognition for authentication and security, industrial robots) or safety-critical applications (e.g., autonomous drones, collaborative robots, self-driving cars and autonomous vehicles for transportation).

The scientific community is hence studying new cost-effective verification and validation techniques tailored to these systems. In particular, automated testing and analysis is a very active area that has led to notable advances to realize the promise of dependable AI-enabled software and systems.

This special issue welcomes contributions regarding approaches, techniques, tools, and experience reports about adopting, creating, and improving automated testing and analysis of AI-enabled software and systems with a special focus on dependability aspects, such as reliability, safety, security, resilience, scalability, usability, trustworthiness, and compliance to standards.

Topics of interest include, but are not limited to:

    Verification and validation techniques and tools for AI-enabled software and systems
    ​Automated testing and analysis approaches, techniques, and tools for AI-enabled software and systems.
    Fuzzing and Search-based testing for AI-enabled software and systems.
    Metamorphic testing for AI-enabled software and systems.
    Techniques and tools to assess the dependability of AI-enabled software and systems, such as reliability, safety, security, resilience, scalability, usability,
    trustworthiness, and compliance with standards in critical domains.
    Fault and vulnerability detection, prediction, and localization techniques and tools for AI-enabled software and systems.
    Automated testing and analysis to improve explainability of AI-enabled software and
    systems.
    Program analysis techniques for AI-enabled software and systems.
    Regression testing and continuous integration for AI components.
    Automated testing and analysis of generative AI, such as Large Language Models
    (LLMs), chatbots, and text-to-image AI systems.
    Verification and validation techniques and tools for specific domains, such as healthcare, telecommunication, cloud computing, mobile, big data, automotive, industrial manufacturing, robotics, cyber-physical systems, Internet of Things, education, social networks, and context-aware software systems.
    Empirical studies, applications, and case studies in verification and validation of AI-enabled software and systems.
    Experience reports and best practices in adopting, creating, and improving testing and analysis of AI-enabled software and systems.
    Future trends in AI testing and analysis, such as integration of AI technologies in test case generation and validation of AI-enabled software and systems.

Important dates (tentative)

Submission Open Date: January 1, 2024
Manuscript Submission Deadline: August 31, 2024
Completion of the review and revision process (final notification): October 31, 2024

Special Issue on Soft Computing for Software Systems and Applications
Submission Date: 2024-09-15

Software with appropriate hardware support is increasingly ubiquitous in our everyday lives and plays an indispensable role in the function of our society. With the exponential demand and growth of software-enabled applications, so too are the functional and behavioral complexities of these systems and the overwhelming consequences of their failures. To ensure that software systems are dependable – reliable, secure, and safe – not only do the existing technologies and tool supports need to be enhanced, but also new strategies and approaches are needed to satisfy more strict requirements on their reliability, security, and safety.

The high complexity and spontaneously changing operating environments of these software systems are the major contributing factors to the problems. Often, conventional computing or analytical models cannot provide an ideal solution. Under such situations, soft computing centric techniques should be used to supply a good approximate solution. This is especially the case with the rise of AI, machine/deep learning, and large language models (LLM), which have launched a wave of technological renovation to address real-world problems that suffer from imprecision and uncertainty. Such a new computational paradigm should be able to learn from past operational profiles and results, and it should also be able to enhance the automation by mapping a human mind into a role model while making informed decisions.

Editors in Chief

    Paris Avgeriou, University of Groningen, Groningen, the Netherlands
    David Shepherd, Louisiana State University, Baton Rouge, Louisiana, USA

Special Issues Editors

    Raffaela Mirandola, Polytechnic of Milan, Milano, Italy
    Laurence Duchien, University of Lille, Villeneuve d'Ascq, France

Guest Editor

Professor W. Eric Wong, University of Texas at Dallas, USA

W. Eric Wong received his M.S. and Ph.D. degrees in computer science from Purdue University, USA. He is a full professor, the Director of Software Engineering Program, and the founding director of the Advanced Research Center for Software Testing and Quality Assurance in Computer Science at the University of Texas at Dallas (UTD), which is one of the major sites of a Security and Software Engineering Research Center (S2ERC) sponsored by the US National Science Foundation under the Industry/University Cooperation Research (NSF I/UCRC) Program. Before joining UTD, he was with Telcordia Technologies (formerly Bellcore) as a senior research scientist and the project manager in charge of Dependable Telecom Software Development. In 2014, Professor Wong was named the IEEE Reliability Society Engineer of the Year. His research focuses on helping practitioners improve the quality of software while reducing the cost of production. In particular, he is working on software testing, debugging, risk analysis/metrics, safety, and reliability. He has very strong experience developing real-life industry applications of his research results. Professor Wong received the Most Influential Paper Award from ICST (IEEE International Conference on Software Testing) and JSS (Journal of Systems and Software) in 2020. He was the Editor-in-Chief of IEEE Transactions on Reliability for two consecutive terms from 2016 to 2022. He has also been a Senior Associated Editor of Elsevier’s JSS since January 2016. In addition, Professor Wong has served as a special guest editor for many international journals.

Special issue information:

This special issue focuses on the quality assurance of software systems and their applications, in particular studies using soft computing techniques. We solicit high-quality work in describing original and unpublished (nor simultaneously submitted to other venues) results of theoretical, empirical, conceptual, and experimental quality assurance research at any point in the software life cycle. We also welcome industrial experience and practice in providing quality assurance for software in various domains, including transportation, finance, healthcare, IoT, blockchains with smart contracts, and cyber-physical systems with intelligent components such as implanted medical devices, autonomous systems like drones and self-driving cars, and application infrastructures for smart cities.

This special issue has an open call to the research community. At the same time, we also encourage authors of top-quality papers accepted by the 24th IEEE International Conference on Software Quality, Reliability, and Security (QRS 2024) to submit their extended articles. Our objective is to (i) provide a summary of research that advances quality assurance for software systems and their applications, and (ii) serve as a collection of current state-of-the-art research and state-of-practice techniques and tool support within this content.

Topics of Interest

Topics of interest focus on the application of soft computing for

    Reliability, Security, and Safety of Software Systems and Applications
    Software Testing, Validation, and Verification
    Program Debugging and Comprehension
    Software Modeling, Simulation, and Evaluation
    Software Defect/Vulnerability Prediction and Analysis
    Software Penetration and Protection
    AI, Machine/Deep Learning, and Large Language Models for Software Quality Assurance
    Information and Knowledge Management
    Internet of Things, Blockchains, Autonomous, and Cyber-Physical Systems
    Empirical Studies and Benchmarking
    Tool Support and Industrial Best Practices

Surveys, literature reviews, and mapping studies are out of the scope of this special issue and will be desk rejected.

Manuscript submission information:

Important Dates

August 1, 2024 - Submission Open
September 15, 2024 - Submission deadline
November 15, 2024 - First-round notification
January 15, 2025 - Revision due
March 15, 2025 - Second-round notification

Special Issue on Software Engineering for Systems-of-Systems and Software Ecosystems
Submission Date: 2024-09-16

Guest editors:
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Prof. Antonia Bertolino, ISTI-CNR, Italy
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Dr. Francesca Lonetti, ISTI-CNR, Italy
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Dr. Pablo Antonino, Fraunhofer IESE, Germany
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Dr. Doo-Hwan Bae, KAIST, South Korea
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Special issue information:
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Systems-of-Systems (SoS) and Software Ecosystems (SECO) attract growing interest. SoS refers to evolving software systems whose constituent systems (themselves systems in their own right) cooperatively work to fulfil a global mission that is beyond their individual scope. SECO consists of an ensemble of software solutions functioning as a unit and often sharing a common platform or market, thus connecting a community of users to a community of solution providers.
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The engineering of SoS and SECO faces similar challenges: in both fields, distributed resources and efforts are shared and combined with the aim of reducing costs of complex systems and reaching new Information Technology markets. The development of both systems is distributed among independent teams cooperating under different levels of mutual awareness and commitment. In either case technical aspects of software development become mixed with social issues. Further, when SoS and SECO are analysed together, Software Engineering-related problems and challenges are amplified and become even more critical.This special issue welcomes submissions addressing all technical aspects, challenges and social issues of SoS and SECO. On this theme, we will run in April the 12th Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems (SESoS 2024), which will be associated with the 46th IEEE/ACM International Conference on Software Engineering (ICSE 2024) in Lisbon, Portugal. The goal of the workshop series is to provide a forum in which researchers and practitioners can exchange ideas and experiences, analyse research and development issues, discuss promising solutions, and propose theoretical foundations for development and evolution of complex systems, inspiring visions for the future of software engineering for SoS and SECO, as well as paving the way for a more structured community effort.
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In this special issue we both invite extended versions of the best papers presented at SESoS 2024, and solicit novel submissions related to the theme. We are interested in challenges and trends for research and practice. Real-world experience reports and industrially-relevant methods and tools are welcome. Relevant topics include, but are not limited to:
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Manuscript submission information:
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Important Dates (tentative)
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Submission Open Date: May 20, 2024
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Submission Deadline: September 16, 2024
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First notification: December 11, 2024
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Revised submission: February 5, 2025
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Final notification: April 30, 2025
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