The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Exploiting machine learning techniques for analyzing programs has attracted much
attention. One key problem is how to represent code fragments well for follow-up analysis …

This paper presents a literature review in the field of summarizing software artifacts, focusing
on bug reports, source code, mailing lists and developer discussions artifacts. From Jan …

Source code summarization involves creating brief descriptions of source code in natural
language. These descriptions are a key component of software documentation such as …

Descriptive names are a vital part of readable, and hence maintainable, code. Recent
progress on automatically suggesting names for local variables tantalizes with the prospect …

During software development and maintenance, developers spend a considerable amount
of time on program comprehension activities. Previous studies show that program …

Every programmer has a characteristic style, ranging from preferences about identifier
naming to preferences about object relationships and design patterns. Coding conventions …

Cross-Project Defect Prediction (CPDP) as a means to focus quality assurance of software
projects was under heavy investigation in recent years. However, within the current state-of …

Information Retrieval (IR) methods, and in particular topic models, have recently been used
to support essential software engineering (SE) tasks, by enabling software textual retrieval …

Researchers in software engineering have attempted to improve software development by
mining and analyzing software repositories. Since the majority of the software engineering …