This paper provides a comprehensive review of the literature concerning the utilization of
Natural Language Processing (NLP) techniques, with a particular focus on transformer …

The significant advancements in Large Language Models (LLMs) have resulted in their
widespread adoption across various tasks within Software Engineering (SE), including …

Large language models (LLMs) pretrained on vast source code have achieved prominent
progress in code intelligence. However, existing code LLMs have two main limitations in …

Recently, the ChatGPT LLM has received great attention: it can be used as a bot for
discussing source code, prompting it to suggest changes, provide descriptions or even …

Unit testing is essential in detecting bugs in functionally-discrete program units. Manually
writing high-quality unit tests is time-consuming and laborious. Although traditional …

We propose and release a new vulnerable source code dataset. We curate the dataset by
crawling security issue websites, extracting vulnerability-fixing commits and source codes …

This paper introduces an innovative approach that combines Large Language Models
(LLMs) with Formal Verification strategies for automatic software vulnerability repair. Initially …

Incident management for cloud services is a complex process involving several steps and
has a huge impact on both service health and developer productivity. On-call engineers …

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 …

Unit testing plays an essential role in detecting bugs in functionally-discrete program units
(eg, methods). Manually writing high-quality unit tests is time-consuming and laborious …