Categorizing Software Defects using Machine Learning
Viktor Stagge · Lund University Publications Student Papers (Lund University) · 2018
We analyze how automatically generated crash reports can be used to aid in the process of software defect categorization. The crash reports are automatically generated logs, which vary widely in both format and in information content. Each crash report used is linked to its corresponding, human-written bug report. The problem is handled as a long text-based classification problem. Several different machine learning techniques are compared. Amongst these is our own Keras-based implementation of a Hierarchical Attention Network, with which we achieved an accuracy of 72.5% on Severity prediction, and an accuracy of 51.4% on Responsible Group prediction.