Automated test case generation aims to improve software testing by reducing the manual effort required to create test cases. Recent advancements in large language models (LLMs), with their ability to understand natural language and generate code, have identified new opportunities to enhance this process. In this review, the focus is on the use of LLMs in test case generation to identify the effectiveness of the proposed methods compared with existing tools and potential directions for future research. A literature search was conducted using online resources, filtering the studies based on the defined inclusion and exclusion criteria. This paper presents the findings from the selected studies according to the three research questions and further categorizes the findings based on the common themes. These findings highlight the opportunities and challenges associated with the use of LLMs in this domain. Although improvements were observed in metrics such as test coverage, usability, and correctness, limitations such as inconsistent performance and compilation errors were highlighted. This provides a state-of-the-art review of LLM-based test case generation, emphasizing the potential of LLMs to improve automated testing while identifying areas for further advancements.

This study examines the impact of this transition on medical students at a public university by focusing on their academic performance, emotional well-being, and adaptability to remote learning environments. Since the COVID-19 pandemic prompted an unprecedented shift from traditional classroom learning to online platforms across global educational systems, it is crucial to understand how students navigated these changes and adapted to the new learning conditions. This study collected qualitative data from 65 medical students through semi-structured interviews. The findings reveal significant effects on study habits, emotional well-being, and curriculum engagement. The findings reveal a mixed response to distance education in medical schools. While some students experienced increased flexibility and, in some instances, improved academic performance, significant challenges also emerged. These challenges included a decline in practical skills, heightened emotional distress, and technical issues that obstruct effective learning. The study emphasizes the necessity for hybrid educational models that combine the advantages of online flexibility with essential in-person interactions, particularly for training practical skills in medical education. It recommends enhancing online educational platforms to better support medical training during crises. Emphasis is placed on developing a robust technical infrastructure alongside comprehensive mental health support systems.