The Journal of Systems and Software publishes papers covering all aspects of programming methodology, software engineering, and related hardware-software-systems issues. Topics of interest include, but are not limited to, software development environments and tools, techniques for developing, validating, and maintaining software systems, software architecture and design, global software development, service orientation, agile approaches, mobile, multiprocessing, real-time, distributed, concurrent, and telecommunications systems, software metrics, reliability models for software, performance issues, and management concerns. The journal publishes research papers, state-of-the-art surveys, and reports of practical experience. All articles should consider the practical application of the idea advanced through case studies, experiments, or systematic comparisons with other approaches already in practice. Occasionally, special issues are devoted to topics of particular interest; proposals for such issues are invited.

Special Issue on Model-Driven Engineering for Software Architecture
Submission Date: 2023-12-29

In today's society, software systems are of great importance, but their growing complexity has created challenges for their design and operation. Software architecture (SA) plays a vital role in the successful development of software systems, as it includes software components, their interactions, and quality attributes. SA facilitates communication, abstraction levels, and enables early software assessment, leading to significant design decisions.Model-Driven Engineering (MDE) has revolutionized the software engineering process by focusing on models as the primary entities throughout the software development life cycle. With the help of software programs known as model transformations, software engineers can automate almost all tasks of software development, resulting in greater efficiency, accuracy, and agility. 
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Recent studies have explored the integration of SA and MDE, suggesting the use of metamodels and modeling languages to represent software architectures formally. This approach allows for early analysis and simulation of SAs using model transformations. The synergy between SA and MDE presents far-reaching implications, including tool reusability for manipulating (meta)models in the context of SAs, and enhancing software development and engineering practices. 
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The purpose of this special issue is to showcase and explore novel approaches that address both current and forthcoming obstacles in the interrelationship between SAs and MDE. 
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Topics of interest are, but are not limited to: 
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- Model-driven techniques and methodologies for designing SA;- Architectural description of SA design, considering logical and physical aspects through MDE;- Model-driven approaches for defining and measuring Quality-of-Service (QoS) in SAs;- Analysis of architectural designs and architectural patterns;- Approaches for migrating legacy architecture towards microservice architectures, server-less employing MDE techniques;- Industrial practices and tools supporting the adoption of MDE into SA;- Methodologies adopted in industrial contexts;- Model repositories for SA;- Code generation from SA models and transformation approaches;- Change Propagation in SA views and consistency management;- Evolution of SA through modeling;- Empirical studies on SA and MDE areas. 
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Surveys, (Systematic) Literature Reviews, Mapping Studies or other approaches which are not related to artifact presentation and evaluation are out of the scope of the special issue and will be desk-rejected. 

Special Issue on Automated Testing and Analysis for Dependable AI-enabled Software and Systems
Submission Date: 2024-04-07

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.
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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).
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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.
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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.