The field of software development is witnessing significant advancements with the integration of Artificial Intelligence (AI) and Large Language Models (LLMs). Recent studies have demonstrated the potential of AI-assisted tools in improving code review, automated unit test generation, and bug detection. One notable trend is the development of LLM-based approaches for automated program repair, which has shown promising results in generating correct and efficient fixes. Additionally, researchers have explored the use of multi-agent systems and finite state machines to improve the accuracy and effectiveness of AI-assisted software development tools. The application of AI and LLMs in software development has also led to improved code quality, reduced debugging time, and enhanced overall productivity. Noteworthy papers in this area include studies on AI-assisted code review, LLM-based unit test generation, and bug detection using multi-agent systems. Notable papers include: AI-Assisted Fixes to Code Review Comments at Scale, which presents a system for providing AI-assisted fixes for code review comments. Input Reduction Enhanced LLM-based Program Repair, which proposes an approach to reduce test inputs for LLM-based program repair. BugScope, which introduces a multi-agent system for bug detection that emulates human auditors.