In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

To implement a program functionality, developers can reuse previously written code
snippets by searching through a large-scale codebase. Over the years, many code search …

The application of information retrieval techniques to search tasks in software engineering is
made difficult by the lexical gap between search queries, usually expressed in natural …

Software engineering practitioners often spend significant amount of time and effort to
debug. To help practitioners perform this crucial task, hundreds of papers have proposed …

This work is motivated by the pervasive use of method invocations in object-oriented (OO)
programs, and indeed their prevalence in patches of OO-program bugs. We propose a …

Bug benchmarks are used in development and evaluation of debugging approaches, eg
fault localization and automated repair. Quantitative performance comparison of different …

The automated task of locating the potential buggy files in a software project given a bug
report is called bug localization. Bug localization helps developers focus on crucial files …

While deep learning is an exciting new technique, the benefits of this method need to be
assessed with respect to its computational cost. This is particularly important for deep …

Debugging is a costly process that consumes much of developer time and energy. To help
reduce debugging effort, many studies have proposed various fault localization approaches …

Bug localization refers to the automated process of locating the potential buggy files for a
given bug report. To help developers focus their attention to those files is crucial. Several …