Zero-shot Learning based Alternatives for Class Imbalanced Learning Problem in Enterprise Software Defect Analysis

Sangameshwar Patil, Balaraman Ravindran

Abstract: Software defect reports are an important type of text data for enterprises as they provide actionable information for improving software quality. Identifying the software defect type automatically can greatly enhance and expedite defect management. Class imbalance is a real-life problem in enterprise software defect classification task and adversely affects the automation effort. We show that zero shot learning based technique can be a good alternative to the well-known supervised learning and SMOTE techniques.