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

Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

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 …

A variety of research methods and techniques are available to SE researchers, and while
several overviews exist, there is consistency neither in the research methods covered nor in …

For tasks like code synthesis from natural language, code retrieval, and code
summarization, data-driven models have shown great promise. However, creating these …

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

Searching and reusing existing code from a large-scale codebase, eg, GitHub, can help
developers complete a programming task efficiently. Recently, Gu et al. proposed a deep …

Code search is an unavoidable activity in software development. Various approaches and
techniques have been explored in the literature to support code search tasks. Most of these …

As code search is a frequent developer activity in software development practices, improving
the performance of code search is a critical task. In the text retrieval based search …

Predictive models are one of the most important techniques that are widely applied in many
areas of software engineering. There have been a large number of primary studies that …