Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …

We review the empirical research on Free/Libre and Open-Source Software (FLOSS)
development and assess the state of the literature. We develop a framework for organizing …

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 …

Natural languages like English are rich, complex, and powerful. The highly creative and
graceful use of languages like English and Tamil, by masters like Shakespeare and …

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 …

Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in
image classification. Despite its popularity, these nonlinear SVMs have a complexity O (n …

Sustainability has emerged as a broad concern for society. Many engineering disciplines
have been grappling with challenges in how we sustain technical, social and ecological …

Code smells are symptoms of poor design and implementation choices that may hinder
code comprehension, and possibly increase changeand fault-proneness. While most of the …

Software development can be challenging because of the large information spaces that
developers must navigate. Without assistance, developers can become bogged down and …

This paper introduces a new technique for finding latent software bugs called change
classification. Change classification uses a machine learning classifier to determine whether …