Journal Information
Journal of Systems and Software (JSS)
http://www.journals.elsevier.com/journal-of-systems-and-software/
Impact Factor:
2.559
Publisher:
Elsevier
ISSN:
0164-1212
Viewed:
18918
Tracked:
47

Call For Papers
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.

Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Metamorphic Testing
Submission Date: 2020-10-30

Testing is the de facto technique for correctness verification and validation in software development. An important aspect of testing is the ability to tell whether the execution results agree with the expected outcomes. The mechanism to determine the correctness of the results is called a test oracle. Unfortunately, such a mechanism often does not always exist, or may exist but cannot be feasibly used, which is known as the oracle problem. This is a major challenge to software testers, severely curtailing the application of testing approaches and methods to practical situations. Metamorphic testing (MT) is a technique that exploits the relationships among the inputs and outputs of multiple program executions. These relationships are called metamorphic relations (MRs). MT has been proved highly effective in testing programs with the oracle problem, for which the correctness of individual output is difficult to determine. Since the introduction of MT, the number of research activities surrounding this technique has grown immensely with numerous applications in various domains such as web services and applications, computer graphics, embedded systems, simulation, and bioinformatics, among others. There is also an increasing trend in domains such as compilers and artificial intelligence-related topics like autonomous vehicles, where MT is being recognized as a key technique. Since the First International Workshop in Metamorphic Testing in 2016, this series of workshops has been held annually, and has been run in conjunction with the International Conference on Software Engineering (ICSE). The aim of this special issue is to document new contributions in MT. We are looking for high-quality submissions on the latest challenges, breakthroughs, and future trends. Topics of interest include, but are not limited to: Emerging and innovative applications of MT (e.g., Artificial Intelligence). Guidelines and techniques for the construction of MRs or MT test cases. Prioritization and minimization of MRs or MT test cases. Quality assessment mechanism for MRs or MT test cases (e.g., metrics). Automated generation of potential MRs. Combination of MRs. Formal methods involving MRs. Case studies from practice. Tools. Empirical studies. Integration/comparison with other techniques. Novel applications, perspectives, or theories inspired by MT.
Last updated by Dou Sun in 2020-02-23
Special Issue on Software Architecture and Artificial Intelligence
Submission Date: 2020-11-09

Artificial intelligence (AI), leveraging machine learning and deep learning, is increasingly seen as a key business enabler for today's software systems including autonomous vehicles, cloud-based services, big data, blockchain, and IoT, among many other industry applications. AI-based systems are heavily reliant on software, with each system growing massively towards having thousands of software components with intricate interdependencies [10]. The AI community has focussed primarily on algorithmic performance and functional aspects of AI-based systems, while neglecting other crucial software quality attributes, including but not limited to observability, maintainability, safety, security, and sustainability. Moreover, as AI-based systems often operate in dynamic, highly complex, and partially observable environments (e.g., cloud-based IoT), an additional challenge in these systems is to ensure that they can continue to deliver high quality of service despite unforeseen changes and uncertainty of operational environments. The software engineering community has faced variations of these challenges in other domains like mobile embedded systems [1] and service-based systems [2]. Evidence shows that one of the most critical success factors for the design and development of these systems has been to raise the level of abstraction by focussing on their software architecture [3, 4]. AI-based systems are software systems. Having an architectural perspective will help software engineers in achieving the sustainable development and deployment of efficient, reliable, and maintainable AI-based systems of the future. Industry experiences also demonstrate that AI-based systems, such as those that have machine learning components, get challenged by unmanaged software design and architectural concerns [5]. Indeed, the architecture of a software system describes its structure in terms of software components, their interaction, and key quality attributes (e.g., safety, performance, maintainability, security) [6]. The architecture of a software system has a significant influence on its key quality attributes and this is especially important for AI-based systems, where reliability, safety, resilience, and privacy are the “make or break” factors of many industrial sectors. This special issue is a step forward towards positioning software architecture at the core of the ability to build and sustain the AI-based systems of the future. Among others, this special issue will contribute with methods, techniques, and tools for supporting software architects and engineers in designing the software architecture of their AI-based systems and ensure that they will meet their quality and functional requirements in addition to case studies where success and failures are shared. Moreover, there is recent research progress that demonstrates application of AI techniques like machine learning, reinforcement learning, deep neural networks to improve typical architecting activities, such as extracting architectural design decisions [7] and predicting the impact of architectural changes [8] [11]. These results are enabled by the large number of publicly available digital artifacts (e.g., GitHub repositories, Stack Overflow discussions, and other social platforms for developers), which allow researchers and practitioners to build rich datasets on which various AI approaches can be tested upon and subsequently validated. We believe that this trend will continue in the future and will attract greater scientific attention towards (i) discovering new architectural knowledge, (ii) building intelligent tools for software architects, and (iii) improving the quality and development process of future software systems. We invite in this special issue articles with innovative and significant contributions to research at the intersection between software architecture and artificial intelligence. We accept submissions of original and previously unpublished papers and we especially encourage the submission of extended papers from the 14th European Conference on Software Architecture (ECSA 2020). Surveys, (Systematic) Literature Reviews or Mapping Studies are out of the scope of the special issue and will be desk-rejected. Topics of interest include, but are not limited to: ● Quality attribute concerns for AI-based systems ● Patterns and tactics for AI-based systems ● Experiences designing and deploying AI-based systems ● Analysis techniques for uncovering architecture issues in AI-based systems ● Verifying and validating AI models as part of system architectures ● Deployment and maintenance of AI-based architectures ● Method and techniques for improving the architecting process of AI-based systems ● Data and related challenges in architecting AI-based systems ● The impact of different algorithms, AI approaches and architecture challenges ● Data for AI research towards improving architecture methods and techniques ● Use of AI to improve architecture, design, conformance and quality ● Monitoring and sustaining AI-based systems architectures ● Iterative and incremental architecting of AI-based systems ● Impact of infrastructure concerns in architecting AI-based systems ● Architecting data/ML pipelines
Last updated by Dou Sun in 2020-06-07
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