The success of deep learning depends on finding an architecture to fit the task. As deep
learning has scaled up to more challenging tasks, the architectures have become difficult to …

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

Large Language Models (LLMs) have demonstrated exceptional coding capability.
However, as another critical component of programming proficiency, the debugging …

Automated Program Repair (APR) attempts to patch software bugs and reduce manual
debugging efforts. Very recently, with the advances in Large Language Models (LLMs), an …

Automated program repair has emerged as a powerful technique to mitigate the impact of
software bugs on system reliability and user experience. This paper introduces RepairAgent …

Machine learning (ML) now pervades the field of Automated Program Repair (APR).
Algorithms deploy neural machine translation and large language models (LLMs) to …

Gradual typing enables developers to annotate types of their own choosing, offering a
flexible middle ground between no type annotations and a fully statically typed language. As …

Automated Program Repair (APR) aims to automatically generate patches for rectifying
software bugs. Recent strides in Large Language Models (LLM), such as ChatGPT, have …

Smart contracts are becoming appealing targets for hackers because of the vast amount of
cryptocurrencies under their control. Asset loss due to the exploitation of smart contract …

Automated program repair (APR) has achieved promising results, especially using neural
networks. Yet, the overwhelming majority of patches produced by APR tools are confined to …