Abstract Automated Program Repair (APR) has attracted significant attention from software
engineering research and practice communities in the last decade. Several teams have …

Automatic program repair (APR) is crucial to improve software reliability. Recently, neural
machine translation (NMT) techniques have been used to automatically fix software bugs …

Automated Program Repair (APR) helps improve the efficiency of software development and
maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder …

With the rapid development and large-scale popularity of program software, modern society
increasingly relies on software systems. However, the problems exposed by software have …

Automated generate-and-validate (GV) program repair techniques (APR) typically rely on
hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a …

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

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 …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

Automated Program Repair (APR) improves soft-ware reliability by generating patches for a
buggy program automatically. Recent APR techniques leverage deep learning (DL) to build …

Automated debugging techniques have the potential to reduce developer effort in
debugging. However, while developers want rationales for the provided automatic …