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

Genetic improvement (GI) uses automated search to find improved versions of existing
software. We present a comprehensive survey of this nascent field of research with a focus …

This paper provides a survey of the emerging area of Large Language Models (LLMs) for
Software Engineering (SE). It also sets out open research challenges for the application of …

Deep learning (DL) techniques are gaining more and more attention in the software
engineering community. They have been used to support several code-related tasks, such …

Millions of open source projects with numerous bug fixes are available in code repositories.
This proliferation of software development histories can be leveraged to learn how to fix …

Statistical language modeling techniques have successfully been applied to large source
code corpora, yielding a variety of new software development tools, such as tools for code …

Code clone detection is an important problem for software maintenance and evolution. Many
approaches consider either structure or identifiers, but none of the existing detection …

The effectiveness of search-based automated program repair is limited in the number of
correct patches that can be successfully generated. There are two causes of such limitation …

Measuring code similarity is fundamental for many software engineering tasks, eg, code
search, refactoring and reuse. However, most existing techniques focus on code syntactical …

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