Visit the journal's proposal guidelines to submit a proposal for a special issue (original contributions on a topic within the scope of the journal) or a special section with extended papers from a conference of workshop within the scope of the journal.

Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal's scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include: 

• Software management, quality and metrics,
• Software processes,
• Software architecture, modelling, specification, design and programming
• Functional and non-functional software requirements
• Software testing and verification & validation
• Empirical studies of all aspects of engineering and managing software development Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information.

The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering. Guidelines for conducting systematic reviews are provided here.

Special Issue on Next-Generation Model-Based Software Engineering with Foundation Models
提出日: 2025-04-30

Guest editors:

Dr. Claudio Di Sipio, Ph.D
University of l’Aquila, L’Aquila, Italy
Areas of Expertise: software engineering, model-driven engineering, recommender systems, AI for SE

Prof. Silvia Abrahão, Ph.D
Universitat Politècnica de València, Valencia, Spain
Areas of Expertise: software engineering, model-driven engineering, empirical software engineering, adaptive user interfaces, human factors.

Dr. Martin Weyssow, Ph.D
Singapore Management University, Singapore
Areas of Expertise: software engineering, deep learning for code, code generation, vulnerability detection, AI for SE

Prof. Fabio Palomba, Ph.D.
University of Salerno, Italy
Areas of Expertise: software engineering, software engineering with and for artificial intelligence, software quality, empirical software engineering, human factors.

Special issue information:

Foundation models (FMs) have revolutionized traditional software engineering (SE), outperforming traditional AI-based systems in different tasks, e.g., code completion, code review, or automatic categorization of software artifacts. Just recently, the model-based engineering (MBE) community has been attracted by the usage of FMs to automatize different tasks, i.e., model completion, model transformation, and development of domain-specific language (DSL). This special issue solicits the application of well-founded FMs, e.g., pre trained models or large language models, to MBE and the related sub-fields such as model-driven engineering (MDE), model-driven development (MDD), and model-driven architecture (MDA). Furthermore, this issue seeks the application of MBE paradigm, technique, and tools to support the design, fine-tuning, and deployment of FMs.

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

    The use of foundation models for supporting the completion of different modeling artifacts
    The use of foundation models for supporting the development of domain-specific language (DSL), abstract and concrete syntax
    The use of foundation models in industrial MBE systems
    The use of foundation models to support software architecture and requirement engineering
    The use of foundation models to support low-code development platforms
    Fine-tuned models for supporting MBE, MDE, MDD, and MDA tasks
    Generation of synthetic datasets for MBE using foundation models
    MBE techniques to support the development of foundation models, pre-trained models, or large language models
    MBE paradigms for specifying prompt engineering techniques for large language models
    Systematic literature review, systematic mapping study, and multi-vocal studies on the intersection between MBE and foundation models
    Empirical studies, experience reports, and surveys on the usage of foundation models for MBE systems

Manuscript submission information:

Authors should prepare their manuscript according to the guide for authors at Guide for Authors - Information and Software Technology

The authors should use the Editorial Manager (EM) site for submitting their articles: Submission site for Information and Software Technology and select “VSI:Next_MBSE” when they reach the “Article Type” step in the submission process.

Final Manuscript Submission Deadline: 30th April 2025

Special Issue on Are you ready for the age of fairness in software systems?
提出日: 2025-04-30

Guest editors:

Dr. Rodrigo Spínola
Virginia Commonwealth University, United States
spinolaro@vcu.edu - www.rodrigospinola.com 

Dr. Ronnie de Souza Santos
University of Calgary, Canada
ronnie.desouzasantos@ucalgary.ca - www.drdesouzasantos.ca 

Special issue information:

Technology plays a crucial role in people’s lives, influencing several aspects of modern society, such as work, education, politics, and leisure. If software engineering does not strive to be inclusive in all its facets (i.e., education, research, and industry), software products might unintentionally constrain groups of users. The expectation that software is effective in representing the multifaceted characteristics of our society has transcended technical needs and now stands as an ethical obligation for developing algorithms and systems that are both equitable and inclusive. In this context, the concept of software fairness emerges as a crucial non-functional requirement and a quality attribute for software, especially those based on data-driven processes. 

Software fairness refers to the ethical principle and practice of ensuring that software systems, algorithms, and their outcomes are just, equitable, and unbiased across different groups of people, regardless of their gender, race, ethnicity, sexual orientation, cultural background, or any other aspect that composes their identity. In software engineering, fairness typically involves preventing discrimination, promoting inclusivity, and mitigating potential biases in the design, development, deployment, and usage of software systems. Though not entirely new to software development, this concept has only recently gained traction, fueled by the escalating discussions surrounding software engineering for artificial intelligence and ethics in machine learning —a scenario that highlights its essential role in understanding the impact of biased software in modern software engineering practices. However, this debate is still evolving slowly. It seems counter-intuitive, but the area responsible for creating innovative software solutions for billions of users worldwide does not reflect the diversity of the society it serves, e.g., algorithms are racist, technical forums are sexist, and the software industry is not welcoming to underrepresented groups.

Many research challenges and opportunities remain to be addressed in this area. This special issue is open to all manuscripts presenting novel and strong contributions to deal with software fairness, including (i) 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 and (ii) empirical studies in the field, addressing one or many human, technical, social, and economic issues of software fairness through qualitative and/or quantitative analyses.

Suggested Topics

The IST Special Issue on Fairness in Software Systems includes, but is not limited to, the following topics of interest:

    Bias in Machine Learning Models: Identifying and mitigating bias in training data; Algorithmic fairness and bias detection techniques; Case studies of bias in deployed systems.
    Fairness in AI and Machine Learning: Definitions and metrics of fairness; Fairness-aware machine learning algorithms; Trade-offs between fairness and other performance metrics.
    Ethical Implications of Software Fairness: Ethical considerations in AI development and deployment; Societal impact of unfair software.
    Transparency and Accountability in Software: Explainable AI and interpretability of machine learning models; Auditing and monitoring AI systems for fairness; Accountability mechanisms for software developers and organizations.
    Data Collection and Preprocessing for Fairness: Techniques for collecting unbiased and representative data; Data preprocessing methods to ensure fairness; Handling missing data and data augmentation for fairness.
    Fairness in Human-Computer Interaction (HCI): Designing user interfaces that promote fairness; User perception and trust in fair AI systems; Inclusive design practices and accessibility considerations.
    Mitigating Fairness Issues in Software Development Lifecycle: Integrating fairness checks in the software development process; Tools and frameworks for developing fair software; Best practices for collaborative and inclusive development teams.
    Evaluating and Benchmarking Fairness: Standard datasets and benchmarks for fairness evaluation; Comparative studies of fairness metrics and algorithms; Real-world deployment and evaluation of fairness interventions.
    Software Fairness Debt: Definition and conceptualization of software fairness debt; Impact of fairness debt on long-term system performance and trust; Identifying and measuring fairness debt; Managing and mitigating fairness debt; Economic and organizational impact of fairness debt; Technical approaches to fairness debt remediation.

Manuscript submission information:

Submission Deadline: 30 April 2025

Special Issue on Model- and Data-driven Digital Twins
提出日: 2025-05-05

Digital Twins (DTs) are virtual replicas of physical assets, systems, or processes that enable realtime monitoring, analysis, and simulation. Their strength lies in the dynamic duality between the real system and its digital representation: a DT is constantly and continuously updated with realtimedata, reflecting changes in the state of its physical counterpart. Thus, the adoption of a DTbased approach allows the execution of an effective and timely what-if analysis to support systems innovation and achieve efficiency gains and cost savings.

The design, implementation, and deployment of DTs might significantly benefit from the adoption of innovative Software Engineering (SE) practices and methodologies that provide appropriate guidance in the DTs development process, ensuring consistency and traceability between the physical system and its digital twin.

Due to the dynamic relationship between the actual system and its digital counterpart, the investigation of innovative data-driven approaches constitutes a prominent challenge in the DTs domain. Networks of sensors and IoT devices capable of measuring and storing large quantity of data are often part of the execution infrastructure of complex systems. Furthermore, their application layer often relies on Process-Aware Information Systems (PAIS) which orchestrates local, web and cloud-based services and captures data regarding the actual process execution in an event log.

Guest editors:

Dr. Paolo Bocciarelli
University of Rome “Tor Vergata”, Italy

Dr. Andrea D’Ambrogio
University of Rome “Tor Vergata”, Italy

Dr. Ghaith Rabadi
University of Central Florida (UCF), Orlando, FL

Dr. Greg Zacharewicz
IMT - Mines Ales (Institut Mines Télécom), France

Special issue information:

This special issue aims to collect high-quality research contributions, reviews and case studies that explore approaches, methodologies and innovative applications in the field of digital twins, with a specific focus on data- and model-driven paradimgs and principles.We invite submissions on the following topics (but not limited to):

    Requirements Engineering​
    Agile and traditional methodologies for the DT development
    Modeling and Simulation
    Simulation model parameterization
    Statistical methods for calibrating simulation parameters from observational data
    Information and Knowledge Management
    Machine Learning and AI techniques for supporting the DTs development
    Mathematical and computational foundations of DTs
    DT Verification and Validation
    Optimization Methods and Models for DTs
    Model-driven and Low-code approaches
    Real-time data management in DTs lifecycle
    CPS and IoT Integration
    Case Studies and Applications

Special Issue on Artificial Intelligence and Service Oriented Computing (AI&SoC)
提出日: 2025-05-31

Guest editors:

Dr. Prof. Claudia Raibulet
DISCo – Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano-Bicocca, Italy
Email: claudia.raibulet@unimib.it

Dr. Slim Kallel
Computer Science Department, University of Sfax, Tunisia
E-mail: slim.kallel@fsegs.usf.tn

Special issue information:

Combining Artificial Intelligence (AI) and Service-Oriented Computing (SoC) as powerful paradigms can significantly enhance the capabilities of modern software systems. This combination offers an important strategy for building intelligent, scalable, and adaptable systems. By leveraging the modularity and the interoperability of SoC, organizations can effectively integrate advanced AI capabilities into their operations, driving innovation and competitive advantage.

SoC promotes interoperability among various software components, supports scalability, improves scalability, ensures the flexibility and the agility of the AI functionalities that can be encapsulated into software components.

The service oriented and AI-based approach can enhance decision-making processes by providing real-time data analysis, predictive analytics, and automated decision support. In addition, SoC can handle sensitive data and maintaining trust in AI-driven processes.

The purpose of this special issue is to gather high-quality research and reviews that highlight the start-of-the-art on combining artificial intelligence and service-oriented computing, as well as to discuss future trends.

We call for original previously unpublished research and industrial papers. papers must contain original work that has neither been previously published nor is currently under review by another journal or conference.

Topics of interest

We encourage the submission of high-quality contributions regarding software engineering aspects for the combination of AI and SoC. Topics of interest include but are not limited to following:

    Combining AI and Engineering Service-Oriented Applications and Cloud Services
    Generative AI as a Software Service
    Lightweight AI-based Services
    Optimising AI Models Using Local Data on Resource-Constrained Edge Devices
    Combining AI and RealTime Service oriented and EMbedded Systems
    Services and Quantum Software Acronym and AI
    AI for Adaptive Service-oriented and Cloud Applications
    AI and Agility with Microservices Programming
    AI-enabled Process Automation
    SoC, AI, and IoT for Smart Applications
    SoC for AI Applications

Manuscript submission information:

Authors should prepare their manuscript according to the guide for authors at 
Guide for Authors - Information and Software Technology

The authors should use the Editorial Manager (EM) site for submitting their articles: Submission site for Information and Software Technology and select “VSI:AI&SoC” when they reach the “Article Type” step in the submission process.

All papers will be peer-reviewed by at least three independent international reviewers. Requests for additional information should be addressed to the corresponding guest editor.

Important Dates

    Submission deadline: May 31st, 2025
    Paper notification: July 31st, 2025
    Submission deadline for revised papers: October 31st, 2025
    Final acceptance/rejection notification: January 31st, 2026

Special Issue on Regulatory Compliance in Software Engineering
提出日: 2025-09-05

Guest editors:

Dr.-Ing. Sallam Abualhaija
University of Luxembourg, Luxembourg
Areas of Expertise: Requirements Engineering, Regulatory Compliance, Quality Assurance, Responsible AI

Dr. Michael Unterkalmsteiner
Blekinge Institute of Technology, Karlskron, Sweden
Areas of Expertise: Requirements Engineering, Testing, Empirical Software Engineering, Regulatory Compliance

Prof. Dr. Daniel Mendez
Blekinge Institute of Technology and fortiss, Karlskrona, Germany
Areas of Expertise: Requirements Engineering, Regulatory Compliance, Empirical Software Enmgineering

Special issue information:

This special issue in the Information and Software Technology journal is intended to provide researchers with a venue to present insights, experiences, innovations and solutions in the study of regulatory compliance in Software Engineering. The engineering of software-intensive products and services is confronted more and more with the challenge of complying with a plethora of regulations emerging from different sources and affecting both engineering processes and product characteristics alike. Regulatory compliance in Software Engineering refers to the verifiable adherence to relevant regulatory artefacts ranging from obligatory (i.e. legally binding) laws to domain-specific standards such as safety and security standards.

All submissions will undergo a rigorous reviewing process. Each submission will be evaluated by at least three independent reviewers who are experts in this field. The review process will assess submissions based on several key criteria, including relevance, significance of contribution, technical quality, scholarship, potential impact, and quality of presentation. Reviewing criteria are detailed at this link.

The guest editors of the special issue are not permitted to submit.

Papers submitted to the Special Issue should present original, unpublished work, relevant to one of its topics, outlined below. Extensions of previously published material are accepted, if the extensions are substantial, and the relation to previously published material is clearly explained within the manuscript. In line with the journal’s policy submissions or any substantially overlapping work must not be under review or published in another journal or conference during the review process.

Submission topics

We particularly invite research papers reporting on in-practice studies and solution proposals as well as elaborate experience reports that critically reflect upon contemporary challenges and practices for regulatory compliance in software engineering (the scope of the submission should become apparent from the abstract). We further invite submissions from interdisciplinary configurations of authors and domains (such as LegalTech, InsurTech, FinTech, or science and technology studies) that address questions of high relevance to the engineering of software-intensive products and services. Topics of interest include, but are not limited to:

    Checking compliance of software artefacts against regulations
    Dealing with changes of software engineering artefacts as well as regulations and how to ensure compliance throughout those changes
    Traceability of regulations to engineering artefacts
    Challenges in modeling regulations (including regulations for specific domains)
    Machine-analyzable representations of regulations
    Ensuring compliance across multiple jurisdictions, domains, and platforms
    Granularity level of legal interpretation
    Change impact analysis of regulatory changes
    Domain-specific compliance checking
    Requirements elicitation from regulations
    Compliance for AI-enabled systems with emphasis on AI regulations
    Legal requirements as non-functional requirements
    Human supervision in the compliance process
    Interpretability - multiple stakeholders use compliance checking systems
    Meta-compliance: the compliance of compliance checking solutions
    Methodological papers on multidisciplinary collaboration
    Theoretical papers on the legal perspective of regulatory compliance
    Risk management and legal accountability within regulatory compliance
    Teaching and education for regulatory compliance