When changes are performed on an automated production system (aPS), new
faults can be accidentally introduced in the system, which are called
regressions. A common method for finding these faults is regression testing. In
most cases, this regression testing process is performed under high time
pressure and on-site in a very uncomfortable environment. Until now, there is
no automated support for finding and prioritizing system test cases regarding
the fully integrated aPS that are suitable for finding regressions. Thus, the
testing technician has to rely on personal intuition and experience, possibly
choosing an inappropriate order of test cases, finding regressions at a very
late stage of the test run. Using a suitable prioritization, this iterative
process of finding and fixing regressions can be streamlined and a lot of time
can be saved by executing test cases likely to identify new regressions
earlier. Thus, an approach is presented in this paper that uses previously
acquired runtime data from past test executions and performs a change
identification and impact analysis to prioritize test cases that have a high
probability to unveil regressions caused by side effects of a system change.
The approach was developed in cooperation with reputable industrial partners
active in the field of aPS engineering, ensuring a development in line with
industrial requirements. An industrial case study and an expert evaluation were
performed, showing promising results.Comment: 13 pages, https://ieeexplore.ieee.org/abstract/document/8320514