Automated error matching system using machine learning and data clustering : Evaluating unsupervised learning methods for categorizing error types, capturing bugs, and detecting outliers.

For large and complex software systems, it is a time-consuming process to manually inspect error logs produced from the test suites of such systems. Whether it is for identifyingabnormal faults, or finding bugs; it is a process that limits development progress, and requires experience. An automated solution for such processes could potentially lead to efficient fault identification and bug reporting, while also enabling developers to spend more time on improving system functionality. Three unsupervised clustering algorithms are evaluated for the task, HDBSCAN, DBSCAN, and X-Means. In addition, HDBSCAN, DBSCAN and an LSTM-based autoencoder are evaluated for outlier detection. The dataset consists of error logs produced from a robotic test system. These logs are cleaned and pre-processed using stopword removal, stemming, term frequency-inverse document frequency (tf-idf) and singular value decomposition (SVD). Two domain experts are tasked with evaluating the results produced from clustering and outlier detection. Results indicate that X-Means outperform the other clustering algorithms when tasked with automatically categorizing error types, and capturing bugs. Furthermore, none of the outlier detection methods yielded sufficient results. However, it was found that X-Means’s clusters with a size of one data point yielded an accurate representation of outliers occurring in the error log dataset. Conclusively, the domain experts deemed X-means to be a helpful tool for categorizing error types, capturing bugs, and detecting outliers