ToCaMS 2020
Wed 28 Oct 2020 Porto, Portugal
co-located with ICST 2020
Wed 28 Oct 2020 12:00 - 12:30 at Miragaia - Session II

Cloud providers offer a wide variety of services to their tenants. Providers share large scale infrastructures to host their services and use configurable software customized with configurations to meet different tenant requirements. These configurations are often the main source of errors. Moreover, they undergo frequent changes, therefore, systems’ compliance to requirements needs to be re-evaluated frequently using regression testing. The problem of regression test case selection has been extensively addressed in the literature, however, existing approaches do not tackle the problem from the configuration perspective. In this paper, we propose a configuration-based method for regression test suite reduction for cloud systems. Our method targets a set of faults summarized in a fault model, and it relies on a classification of configuration parameters based on their relation to the deployment environment. Our idea is that the relation of the configuration parameters to the environment can be explored to reduce the regression test suite.

Wed 28 Oct

Displayed time zone: Lisbon change

11:00 - 12:30
Session IIToCaMS 2020 at Miragaia
11:00
30m
Full-paper
Towards a Deep Learning Model for Vulnerability Detection on Web Application Variants
ToCaMS 2020
Ana Fidalgo LASIGE, Faculdade de Ciências da Universidade de Lisboa, Ibéria Medeiros LaSIGE, Faculdade de Ciências da Universidade de Lisboa, Paulo Antunes LASIGE, Faculdade de Ciências da Universidade de Lisboa, Nuno Neves DI FC UL
Link to publication DOI
11:30
30m
Full-paper
Test Design with the Classification Tree Method in Presence of Variants
ToCaMS 2020
Vladimir Schmidt Expleo Germany GmbH, Berlin, Peter M. Kruse Expleo Group
Link to publication DOI
12:00
30m
Full-paper
Regression Test Suite Reduction for Cloud Systems
ToCaMS 2020
Oussama Jebbar Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, Mohamed Aymen Saied Concordia University, Ferhat Khendek  Concordia University, Maria Toeroe Ericsson Inc, Montreal
Link to publication DOI