Complexity based test cases for log file analyzers
Esa Heikkinen, Timo D. Hämäläinen · 2017
Log files are used in many big data applications. If the log is meant for a different purpose, the analysis and finding the best log analyzer can be very complex. Our solution is to create a generic test case framework to model and create representative log data. Related work model the behavior as state machines, but our model uses a composition of elementary acyclic graphs, thus addressing the log file size, variation, branching and confidence. We have created test cases originally based on real Intelligent Transportation Systems (ITS) data, and evaluated our LOGDIG analyzer against it. We can easily generate hundreds of test cases with our model, and modify the cases as needed.