Journal Information
Journal of Systems and Software (JSS)
http://www.journals.elsevier.com/journal-of-systems-and-software/
Impact Factor:
2.278
Publisher:
ELSEVIER
ISSN:
0164-1212
Viewed:
16285
Tracked:
43

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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 2018-07-23
Special Issues
Special Issue on Software and Systems Reuse in the Big Data Era
Submission Date: 2019-10-24

Software reuse is an established key-solution for increasing software development productivity and decreasing the number of software defects. In light of the large data footprint that is produced along software development and evolution, in terms of both process (e.g., feature requests, issue tracking, emails, developers’ communication, etc.) and product data (e.g., commits, source code elements, design artifacts, quality metrics, etc.), we identify an opportunity to revisit or reintroduce reuse-related practices, methods, tools, and empirical evidence facilitating the aforementioned big data sources. Driven by this opportunity, the theme of the 18thInternational Conference on Software and System Reuse,ICSR 2019conference is: “Software and Systems Reuse in the Big Data Era”. In the special issue on Software and Systems Reuse in the Big Data Era, we invite submissions on new and innovative research results and industrial experience papers in the area of software and systems reuse. Submissions could deal with all aspects of software and systems reuse, including, but not limited to, the following topics of interest: Approaches facilitating reuse in industry Technical debt and reuse Economic models and metrics to quantify reuse costs and benefits, including risk analysis Human, social, and legal aspects and distribution issues of reusable software Domain analysis, context analysis, and architecture-centric reuse Component-based reuse techniques Generative, systematic, and opportunistic reuse Reverse engineering of potentially reusable components Reusability models and metrics Evolution and maintenance of reusable assets Dynamic aspects of reuse (e.g., post-deployment time) Software documentation and reuse of development knowledge (e.g., API knowledge) Traceability of software artifacts and coarse-grained software reuse Retrieval of reusable artifacts and knowledge in large-scale software repositories (e.g., open-source and industrial code bases) Reuse in software ecosystems, model-driven engineering, multi-discipline teams, open-source systems, agile projects, and safety-critical and mission-critical systems Reuse in emerging practices, e.g., cloud computing, big data applications, IoT, cyber-physical systems, socio-technical systems, smart contracts, block chains, etc. Data collection, analysis, and visualization for software reuse Code generation and recommendation of reusable artifacts (e.g., sample code, APIs)
Last updated by Dou Sun in 2019-04-12
Special Issue on Machine Learning Techniques for Software Quality Evaluation
Submission Date: 2019-11-15

The assessment of software quality is one of the most multifaceted (e.g., structural quality, product quality, process quality, etc.) and subjective aspects of software engineering (since in many cases it is substantially based on expert judgement). Such assessments can be performed at almost all phases of software development (from project inception to maintenance) and at different levels of granularity (from source code to architecture). However, human judgment is: (a) inherently biased by implicit, subjective criteria applied in the evaluation process, and (b) its economical effectiveness is limited compared to automated or semiautomated approaches. To this end, researchers are still looking for new, more effective methods of assessing various qualitative characteristics of software systems and the related processes. In recent years, we have been observing a rising interest in adopting various approaches to exploiting machine learning (ML) and automated decision-making processes in several areas of software engineering. These models and algorithms help to reduce effort and risk related to human judgment in favour of automated systems, which are able to make informed decisions based on available data and evaluated with objective criteria. Thus, the adoption of machine learning techniques seems to be one of the most promising ways to improve software quality evaluation. Conversely, learning capabilities are increasingly often embedded within software, including in critical domains such as automotive and health. This calls for the application of quality assurance techniques to ensure the reliable engineering of ML-based software systems. As such, the special issue invites submissions on new and innovative research results and industrial experience papers in the area of machine learning applications for software quality evaluation. We especially encourage submission of extended papers from the 3rd International Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2019). Submissions could deal with all aspects of the problem, including, but not limited to, the following topics of interest: • Application of machine-learning in software quality evaluation, • Analysis of multi-source data, • Knowledge acquisition from software repositories, • Adoption and validation of machine learning models and algorithms in software quality, • Decision support and analysis in software quality, • Prediction models to support software quality evaluation, • Validation and verification of learning systems, • Automated machine learning, • Design of safety-critical learning software, • Integration of learning systems in software ecosystems.
Last updated by Dou Sun in 2019-07-27
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