BULNER: BUg Localization with word embeddings and NEtwork Regularization

TL;DR

BULNER is a bug localization method that leverages word embeddings and network regularization to improve accuracy in identifying relevant code components from bug reports, outperforming existing techniques.

Contribution

This paper introduces BULNER, a novel bug localization approach combining word embeddings with network regularization, which enhances performance over current state-of-the-art methods.

Findings

BULNER outperforms two leading bug localization methods.

Preliminary results show improved accuracy.

The approach effectively utilizes word embeddings and network regularization.

Abstract

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.