… Bug localization helps developers focus on crucial files. However, the existing automated bug
localization … Specifically, the terms used in bug reports to describe a bug are different from …

… a deep-learning-based bug localization model to the software industry. • We evaluate a deep
learning model (CNN) against a traditional ML model (Simple Logistic) on bug localization …

… deep learning approaches have been applied in bug localization by extracting lexical features
in bug … , we present a bug localization system CAST, which exploits deep learning and …

… For this, we propose a novel algorithm for bug localization and repair. In bug localization,
we perform feature extraction from bug reports and the program source code. In details, we …

… on crossproject bug localization. We highlight that existing supervised bug localization
techniques are … The comparison results suggest that our deep learning model achieves the best …

… for bug localization from a bug report face a key challenge, called lexical mismatch, in which
the terms used in bug reports to describe a bug are … that uses deep neural network (DNN) in …

… bug is and how to fix it. In this work, we present the first deep learning based technique that
can localize bugs in … To localize the bugs, we analyze the trained network using a state-of-the-…

… underutilized by existing bug localization approaches. Aims: We propose DeepLocator, a
novel deep learning based model to improve the performance of bug localization by making …

… the performance of bug localization, we propose DeepLoc, a novel deep learning-based …
describe DeepLoc, a deep learning-based model that automatically localizes buggy files for …

… With the development of deep learning, it can effectively solve the aforementioned lexical
mismatch problem. For example, Lam et al. (2017) proposed the DNNLoc combining rVSM (…