Objective
The demand for larger and more interconnected software systems is constantly increasing, but the ability of developers to satisfy it is not evolving accordingly. The most limiting factor is software validation, which typically requires very costly and complex testing processes. This project aims at significantly improving the efficiency and effectiveness of the testing process and, with it, the overall quality of large software systems. For this, we propose to apply the “divide-and-conquer” principle, which is commonly used for architecting complex software, to testing by developing a novel test orchestration theory and toolbox enabling the creation of complex test suites as the composition of simple testing units. This test orchestration mechanism is complemented with a number of tools that include: (1) Capabilities for the instrumentation of the Software under Test enabling to reproduce real-world operational conditions thanks to features such as Packet Loss as a Service, Network Latency as a Service, Failure as a Service, etc. (2) Reusable testing services solving common testing problems including Browser Automation as a Service, Sensor Emulator as a Service, Monitoring as a Service, Security Check as a Service, Log Ingestion and Analysis as a Service, Cost Modeling as a Service, etc. (3) Cognitive computing and machine learning mechanisms suitable for ingesting large amounts of knowledge (e.g. specifications, logs, software engineering documents, etc.) and capable of using it for generating testing recommendations and answering natural language questions about the testing process. The ElasTest platform thus created shall be released basing on a flexible Free Open Source Software and a community of users, stakeholders and contributors shall be grown around it with the objective of transforming ElasTest into a worldwide reference in the area of large software systems testing and of guaranteeing the long term sustainability of the project generated results.
Fields of science
- natural sciencescomputer and information sciencesdata sciencedata analysis
- natural sciencescomputer and information sciencessoftwaresystem software
- social sciencesother social sciencessocial sciences interdisciplinarysustainable development
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencessoftwaresoftware development
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Topic(s)
Call for proposal
H2020-ICT-2016-1
See other projects for this call
Funding Scheme
RIA - Research and Innovation actionCoordinator
28933 Mostoles
Spain
Participants (10)
80686 Munchen
10623 Berlin
Participation ended
20123 Milano
00185 Roma
28223 Pozuelo De Alarcon
28037 Madrid
8401 Winterthur
28290 Las Rozas De Madrid
Dublin 4 Ballsbridge
17674 Athina
