Log Anomaly Detection via Vector Space Modeling

The patent segments the large volume of test log data into individual log lines and further into tokens (words or phrases). Each log line is processed independently, converting text into numerical vectors. This segmentation transforms the overwhelming bulk data into manageable discrete units that can be analyzed systematically using vector space modeling and anomaly detection algorithms.

The patent replaces the manual mechanical process of log analysis with an automated computational system. The system uses vector space modeling to convert log lines into numerical representations, then applies anomaly detection algorithms to automatically identify problematic patterns. This substitution eliminates human fatigue and error while processing vast volumes of logs quickly, maintaining high accuracy through systematic mathematical comparison against learned normal patterns.

The patent implements feedback by continuously learning from test log patterns. The system builds a vector space model from historical logs representing normal test behavior, then uses this model to detect anomalies in new logs. When anomalies are detected, the system provides feedback by highlighting specific deviating log lines, guiding developers to the root cause. This feedback loop enables efficient root cause identification even as test case numbers grow, because the system adapts to new patterns while maintaining awareness of baseline behavior.