1 BACKGROUND AND MOTIVATION
Predicting the defect type of a software defect is valuable for enterprises as it supports proactive issue resolution, efficient bug triaging, optimization of testing resource allocation, and also helps in root cause analysis. Enterprises typically rely on supervised machine learning algorithms as well as their advanced variants proposed in literature (e.g., [6], [7]) to train models on historical data of software defect reports. These techniques get adversely impacted due to class imbalance challenge present in the real-life software defects. Class imbalance can be costly for an enterprise in terms of both financial resources and reputation. This impacts defect management in SDLC and inflates the maintenance cost.