To support software developers in finding and fixing software bugs, several automated
program repair techniques have been introduced. Given a test suite, standard methods …

In the past decade, research on test-suite-based automatic program repair has grown
significantly. Each year, new approaches and implementations are featured in major …

Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

Due to the promising future of Automated Program Repair (APR), researchers have
proposed various APR techniques, including heuristic-based, template-based, and …

Neural machine translation (NMT) architectures have achieved promising results for
automatic program repair. Yet, they have the limitation of generating low-quality patches (eg …

OpenAI's Codex, a GPT-3 like model trained on a large code corpus, has made headlines in
and outside of academia. Given a short user-provided description, it is capable of …

The problem of fixing errors in programs has attracted substantial interest over the years.
The key challenge for building an effective code fixing tool is to capture a wide range of …

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial
role in software development and maintenance. With the recent advances in deep learning …

A large body of the literature of automated program repair develops approaches where
patches are generated to be validated against an oracle (eg, a test suite). Because such an …

Learning-based program repair has achieved good results in a recent series of papers. Yet,
we observe that the related work fails to repair some bugs because of a lack of knowledge …