Improving the accuracy of duplicate bug report detection using textual similarity measures
Alina Lazar, Sarah Ritchey, Bonita Sharif · 2014
The paper describes an improved method for automatic duplicate bug report detection based on new textual similarity features and binary classification. Using a set of new textual features, inspired from recent text similarity research, we train several binary classification models. A case study was conducted on three open source systems: Eclipse, Open Office, and Mozilla to determine the effectiveness of the improved method. A comparison is also made with current state-of-the-art approaches highlighting similarities and differences. Results indicate that the accuracy of the proposed method is better than previously reported research with respect to all three systems.