This article presents a survey on automatic software repair. Automatic software repair
consists of automatically finding a solution to software bugs without human intervention. This …

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 …

Automated Program Repair (APR) aims to help developers automatically patch software
bugs. However, current state-of-the-art traditional and learning-based APR techniques face …

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 …

During Automated Program Repair (APR), it can be challenging to synthesize correct
patches for real-world systems in general-purpose programming languages. Recent Large …

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 …

Code completion models have made significant progress in recent years, yet current popular
evaluation datasets, such as HumanEval and MBPP, predominantly focus on code …

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 …

Debugging software failures is still a painful, time consuming, and expensive process. For
instance, recent studies showed that debugging activities often account for about 50% of the …

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 …