  There is a growing body of knowledge in the computer science, software
engineering, software testing and software test automation disciplines.
However, a challenge for researchers is to evaluate their research findings,
ideas and tools due to lack of realistic data. This paper presents the Westermo
test system performance data set. More than twenty performance metrics such as
CPU and memory usage sampled twice per minute for a month on nineteen test
systems driving nightly testing of cyber-physical systems has been anonymized
and released. The industrial motivation is to spur work on anomaly detection in
seasonal data such that one may increase trust in nightly testing. One could
ask: If the test system is in an abnormal state - can we trust the test
results? How could one automate the detection of abnormal states? The data set
has previously been used by students and in hackathons. By releasing it we hope
to simplify experiments on anomaly detection based on rules, thresholds,
statistics, machine learning or artificial intelligence, perhaps while
incorporating seasonality. We also hope that the data set could lead to
findings in sustainable software engineering.
