In this article, we propose a hierarchical fault localization technique using a deep neural
network (DNN). First, we prioritize the functions of a program based on their suspiciousness …

Context: The recent progress of deep learning has shown its promising learning ability in
making sense of data, and many fields have utilized this learning ability to learn an effective …

Many fault localization approaches recently utilize deep learning to learn an effective
localization model showing a fresh perspective with promising results. However, localization …

We propose the application of a modified radial basis function neural network in the context
of software fault localization, to assist programmers in locating bugs effectively. This neural …

With software's increasing scale and complexity, software failure is inevitable. To date,
although many kinds of software fault localization methods have been proposed and have …

Numerous fault localization techniques identify suspicious statements potentially
responsible for program failures by discovering the statistical correlation between test results …

Software failure is inevitable with the increase in scale and complexity of the software.
Existing fault localization techniques based on neural networks take statement coverage …

Manually locating and fixing faults can be tedious and hard. Recent years have seen much
progress in automated techniques for fault localization. A particularly promising approach is …

Fault localization is possibly the most time consuming and tedious task in the process of
program debugging. To alleviate this issue, we propose an ensemble of fault localization …

Software testing is always an effective method to show the presence of bugs in programs,
while debugging is never an easy task to remove a bug from a program. To facilitate the …