Modern manufacturing systems based on cyber-physical systems with a growing amount of software allow frequent updates and reconfigurations to adapt the systems to volatile usage scenarios in the production. A diverse system environment arises even for similar or equal subsystems based on the same platform used at different locations. A major challenge for such systems is the regression test after changes or updates. The resources for the regression test, in a dedicated test environment or deployed to the assembly lines, are limited. To plan the test in the best possible way, a lot of dependencies, relationships and experiences from former tests and tests from other locations have to be considered. This paper describes an assistance system which supports the planning of the regression test in such distributed manufacturing scenarios by combining manual modeling with automated data processing. Therefore the system calculates a cross-location test progress and suggests a prioritized test case sequence.
%0 Generic
%1 noauthororeditor2016combined
%A Abele, Sebastian
%A Weyrich, Michael
%B 14th IEEE International Conference on Industrial Informatics (IEEE INDIN 2016) 18.07.-21.07.2016 Futuroscope-Poitiers, France
%D 2016
%K 2016ias 2016kiundmachinelearning ias testfallpriorisierung
%R 10.1109/INDIN.2016.7819324
%T A Combined Fault Diagnosis and Test Case Selection Assistant for Automotive End-of-Line Test Systems
%U https://www.ias.uni-stuttgart.de/dokumente/publikationen/2016_A_Combined_Fault_Diagnosis_and_Test_Case.pdf
%X Modern manufacturing systems based on cyber-physical systems with a growing amount of software allow frequent updates and reconfigurations to adapt the systems to volatile usage scenarios in the production. A diverse system environment arises even for similar or equal subsystems based on the same platform used at different locations. A major challenge for such systems is the regression test after changes or updates. The resources for the regression test, in a dedicated test environment or deployed to the assembly lines, are limited. To plan the test in the best possible way, a lot of dependencies, relationships and experiences from former tests and tests from other locations have to be considered. This paper describes an assistance system which supports the planning of the regression test in such distributed manufacturing scenarios by combining manual modeling with automated data processing. Therefore the system calculates a cross-location test progress and suggests a prioritized test case sequence.
@conference{noauthororeditor2016combined,
abstract = {Modern manufacturing systems based on cyber-physical systems with a growing amount of software allow frequent updates and reconfigurations to adapt the systems to volatile usage scenarios in the production. A diverse system environment arises even for similar or equal subsystems based on the same platform used at different locations. A major challenge for such systems is the regression test after changes or updates. The resources for the regression test, in a dedicated test environment or deployed to the assembly lines, are limited. To plan the test in the best possible way, a lot of dependencies, relationships and experiences from former tests and tests from other locations have to be considered. This paper describes an assistance system which supports the planning of the regression test in such distributed manufacturing scenarios by combining manual modeling with automated data processing. Therefore the system calculates a cross-location test progress and suggests a prioritized test case sequence.},
added-at = {2019-12-17T09:45:04.000+0100},
author = {Abele, Sebastian and Weyrich, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/215e1d14e7701373da5d1c029ab8f8672/taylansngerli},
booktitle = {14th IEEE International Conference on Industrial Informatics (IEEE INDIN 2016) 18.07.-21.07.2016 Futuroscope-Poitiers, France},
doi = {10.1109/INDIN.2016.7819324},
interhash = {be3f657318fe4c10cac6083bb7365c79},
intrahash = {15e1d14e7701373da5d1c029ab8f8672},
keywords = {2016ias 2016kiundmachinelearning ias testfallpriorisierung},
timestamp = {2020-05-18T13:07:56.000+0200},
title = {A Combined Fault Diagnosis and Test Case Selection Assistant for Automotive End-of-Line Test Systems},
url = {https://www.ias.uni-stuttgart.de/dokumente/publikationen/2016_A_Combined_Fault_Diagnosis_and_Test_Case.pdf},
year = 2016
}
