Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

Pre-trained large language models (LLMs) have recently emerged as a breakthrough
technology in natural language processing and artificial intelligence, with the ability to …

Although large language models (LLMs) have demonstrated remarkable code-generation
ability, they still struggle with complex tasks. In real-world software development, humans …

Artificial Intelligence (AI) integration in test oracles provides an unprecedented means to
apply intelligent decision-making to automated software testing. This research describes the …

Testing plays a pivotal role in ensuring software quality, yet conventional Search Based
Software Testing (SBST) methods often struggle with complex software units, achieving …

General large language models (LLMs), represented by ChatGPT, have demonstrated
significant potential in tasks such as code generation in software engineering. This has led …

This research explored real-time fault prediction and recovery using artificial intelligence (AI)
to develop a healing automation testing framework. Despite the growing importance of …

Property-based testing (PBT), while an established technique in the software testing
research community, is still relatively underused in real-world software. Pain points in writing …

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 …

Generating unit tests is a crucial task in software development, demanding substantial time
and effort from programmers. The advent of Large Language Models (LLMs) introduces a …