Evaluation of Automotive Data mining and Pattern Recognition Techniques for Bug Analysis

In an automotive infotainment system, while analyzing bug reports, developers have to spend significant time on reading log messages and trying to locate anomalous behavior before identifying its root cause. The log messages need to be viewed in a Traceviewer tool to read in a human readable form and have to be extracted to text files by applying manual filters in order to further analyze the behavior. There is a need to evaluate machine learning/data mining methods which could potentially assist in error analysis. One such method could be learning patterns for “normal” messages. “Normal” could even mean that they contain keywords like “exception”, “error”, “failed” but are harmless or not relevant to the bug that is currently analyzed. These patterns could then be applied as a filter, leaving behind only truly anomalous messages that are interesting for analysis. A successful application of the filter would reduce the noise, leaving only a few “anomalous” messages. After evaluation of the researched candidate algorithms, two algorithms namely GSP and FP Growth were found useful and thus implemented together in a prototype. The prototype implementation overall includes processes like pre-processing, creation of input, executing algorithms, creation of training set and analysis of new trace logs. Execution of prototype resulted in reducing manual effort thus achieving the objective of this thesis work