BULNER: BUg Localization with word embeddings and NEtwork Regularization
This addresses the costly and tedious problem of bug localization for software engineers, but appears incremental as it builds on existing techniques.
The paper tackles bug localization from bug reports by proposing BULNER, a method using word embeddings and network regularization, which shows better performance than two state-of-the-art methods in preliminary results.
Bug localization (BL) from the bug report is the strategic activity of the software maintaining process. Because BL is a costly and tedious activity, BL techniques information retrieval-based and machine learning-based could aid software engineers. We propose a method for BUg Localization with word embeddings and Network Regularization (BULNER). The preliminary results suggest that BULNER has better performance than two state-of-the-art methods.