This project aims to form an international and inter-sectoral research network via bidirectional secondments focusing on SQA for µDevOps engineering processes in 4 key drivers – Context Learning via AI/ML, Continuous in vivo testing for service-based software in agile/DevOps processes, and risk assessment. Moreover, the project aims to take a step forward by supporting the development of a new process embedding methods for context-driven, in vivo and risk-based Software Quality Assurance (SQA) by testing tailored for μDevOps paradigms.

Dynamic context learning

Studying and developing new modelling and learning strategies for dynamic characterization of the operational context of the microservice-based software under test at runtime, in terms of: usage profile (in terms of end-user demands), microservice architecture (MSA) and interactions (i.e.: structural and behavioral view), and failing behaviour (i.e., the quality experienced by users how the application is expected to fail)

Continuous in vivo testing

Defining and developing techniques for in vivo tests generation and execution for μDevOps environment: techniques shall exploit contextual information at runtime to both generate and execute new test cases, either periodically or on-demand – e.g. at each release cycle or incrementally whenever a context change. QoS of interest in the project are: reliability, performance and security.

Risk assessment

Studying and developing metrics and methods to provide quantitative measures of the business risk of using a given functionality. The risk value will be assessed by statistical-estimators-based techniques (using test results and the usage profile) and by simulation methods built upon all the contextual information that captures the different usage conditions.

Testing process definition, deployment and validation

Defining and developing techniques for in vivo tests generation aProviding both μDevOps-based development and testing as a service. Such a proof-of-concept platform will also support the validation of the process on real-world case studies.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 871342