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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/53593

Title: RETORCH: Resource-Aware End-to-End Test Orchestration
Author(s): Augusto Alonso, Cristian
Morán Barbón, Jesús
Bertolino, Antonia
Riva Álvarez, Claudio A. de la
Tuya González, Pablo Javier
Keywords: Software testing
Continuous integration
Continuous testing
Testing in the cloud
Issue date: 2019
Publisher: Springer
Publisher version: https://doi.org/10.1007/978-3-030-29238-6_22
Abstract: Continuous integration practices introduce incremental changes in the code to both improve the quality and add new functionality. These changes can introduce faults that can be timely detected through continuous testing by automating the test cases and re-executing them at each code change. However, re-executing all test cases at each change may not be always feasible, especially for those test cases that make heavy use of resources thoroughly like End-to-End test cases that need a complex test infrastructure. This paper is focused on optimizing the usage of the resources employed during End-to-End testing (e.g., storage, memory, web servers or tables of a database, among others) through a resource-aware test orchestration technique in the context of continuous integration in the cloud. In order to optimize both the cost/usage of resources and the execution time, the approach proposes to (i) identify the resources required by the End-to-End test cases, (ii) group together those tests that need the same resources, (iii) deploy the tests in both dependency isolated and elastic environments, and (iv) schedule their parallel execution in several machines
Description: 12th International Conference, QUATIC 2019, Ciudad Real, Spain, September 11–13, 2019
URI: http://hdl.handle.net/10651/53593
ISBN: 978-3-030-29237-9
Sponsored: This work was supported in part by the Spanish Ministry of Economy and Competitiveness under TestEAMoS (TIN2016-76956-C3-1-R) project and ERDF funds, and by the European Project ElasTest in the Horizon 2020 research and innovation program (GA No. 731535)
Project id.: TIN2016-76956-C3-1-R
GA No. 731535
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