  This study explores the application of deep learning technologies in software
development processes, particularly in automating code reviews, error
prediction, and test generation to enhance code quality and development
efficiency. Through a series of empirical studies, experimental groups using
deep learning tools and control groups using traditional methods were compared
in terms of code error rates and project completion times. The results
demonstrated significant improvements in the experimental group, validating the
effectiveness of deep learning technologies. The research also discusses
potential optimization points, methodologies, and technical challenges of deep
learning in software development, as well as how to integrate these
technologies into existing software development workflows.
