Track: Quality Aspects in DevOps Development
DevOps is recently emerging as a disruptive series of principles and practices that reduce the amount of time between software refactoring and operationally deploying changes. DevOps principles and tools also primarily aim at strengthening the collaboration between software development and operations engineers in the process of speedily making a design refactoring actionable in operations as well.
On one hand, the goal of this tighter collaboration is to deliver the software product faster to its production environment, by whatever means, procedures, or tools. On the other hand, establishing and certifying the quality of outcoming software and processes is strained by the “need for speed”.
For example, one of the pillars of DevOps approaches for building software fast is the utilisation of automations along its creation toolchain, passing through testing, packaging, release, deployment, monitoring, and runtime management. The achievement of these automations is assisted by techniques for software continuous integration, continuous delivery, infrastructure-as-code, and more. However, the quality assessment of both software product and development process, which in traditional software lifecycles are relatively mature fields, in DevOps contexts suffers from the presence of automated steps that cannot be trivially analyzed with conventional means. Hence, DevOps quality assurance techniques are far from being mature, and are often limited to expensive trial-and-error exercises.
This thematic track of the QUATIC 2018 conference seeks to shed light over the synergies and challenges in DevOps quality engineering. In so doing, we seek novel contributions on any quality aspects, quality evaluations, fallacies, or pitfalls arising or playing a role in the context of DevOps.
The suggested topics of interest include, but are not limited to:
- Methods and models for software quality assessment in DevOps
- Techniques for software quality improvement in DevOps
- Tools for quality assessment and/or improvement during DevOps developments
- Experiences on the impact of software quality engineering in DevOps
- Experiences on the impact of DevOps practices in consolidated Quality engineering processes.
- Emergent Quality Properties of DevOps Architectures
- Analysing, Testing, or otherwise assessing the Quality of DevOps Processes
- DevOps Tools, that is, tools or technical approaches that fit in the DevOps paradigm
- Qualities of skills and human aspects of DevOps engineers
- Quality and Quality assessment of DevOps processes
- Quality of Education of DevOps engineers
- Continuous Aspects of Quality Assurance
- Qualities changed or introduced by DevOps practices, e.g., quality of Infrastructures and quality of Infrastructure-as-Code
- Quality of DevOps Architectural Styles, e.g., Microservices Quality
- Diego Perez-Palacin, Linnaeus University, Sweden
- Radu Calinescu, University of York
- Mauro Caporuscio, Linnaeus University
- Michele Ciavotta, University of Milano Bicocca
- Martin Garriga, Politecnico di Milano
- Anne Koziolek, Karlsruhe Institute of Technology
- John Klein, SEI / CMU
- Philipp Leitner, Chalmers University Gothenburg
- José Merseguer, Universidad de Zaragoza
- Raffaela Mirandola, Politecnico di Milano
- Fabio Palomba, University of Zurich
- Uwe Zdun, University of Vienna
- Vittorio Cortellessa, Universita' dell'Aquila
- Cesar Pardo Calvache, Universidad del Cauca, Colombia
- Oscar Pedreira Fernández, Universidade da Coruña
Diego Perez-Palacin, Linnaeus University, Sweden. Diego Perez-Palacin received the PhD degree in computer science from the University of Zaragoza, Spain. He is Senior Lecturer in the Computer Science Department at Linnaeus University, Sweden. Before, he has been postdoctoral researcher at Politecnico di Milano, Italy, on the research program for Quality Impact Prediction for Evolving Service-Oriented Software and senior researcher at University of Zaragoza. His research interests are in the areas of quality properties of software with special interest in Software Performance Engineering, model-based evaluation, formal methods and self-adaptive software.