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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.</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-07-14</div> </div> </div> <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-list-alt"></i> Special Issues</div> </div> <div class="portlet-content"> <a name="5410"></a><pre>Special Issue on Are you ready for the age of fairness in software systems?</br>Submission Date: 2025-03-30</br></br>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.</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-12-13</div><a name="5408"></a><pre>Special Issue on Artificial Intelligence and Service Oriented Computing (AI&SoC)</br>Submission Date: 2025-03-31</br></br>Guest editors: Dr. Prof. Claudia Raibulet DISCo – Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano-Bicocca, Italy Email: claudia.raibulet@unimib.it Dr. Prof. 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: March 31st, 2025 Paper notification: July 31st, 2025 Submission deadline for revised papers: October 31st, 2025 Final acceptance/rejection notification: January 31st, 2026</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-12-13</div><a name="5412"></a><pre>Special Issue on Model- and Data-driven Digital Twins</br>Submission Date: 2025-05-05</br></br>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</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-12-13</div></div> </div>  <div class="portlet" id="yw3"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-file"></i> Related Journals</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw4" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw4_c0">CCF</th><th id="yw4_c1">Full Name</th><th id="yw4_c2">Impact Factor</th><th id="yw4_c3">Publisher</th><th id="yw4_c4">ISSN</th></tr> </thead> <tbody> <tr class="odd"> <td><span class="badge badge-warning">c</span></td><td><a href="/journal/315">IEEE Transactions on Cloud 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