This study explores the application of deep learning technologies in software development processes, focusing on automating code reviews, error prediction, and test generation to enhance code quality and development efficiency. Through empirical studies, the research compares experimental groups using deep learning tools with control groups using traditional methods in terms of code error rates and project completion times. The results demonstrate significant improvements in the experimental group, validating the effectiveness of deep learning technologies. The study also discusses potential optimization points, methodologies, and technical challenges, as well as how to integrate these technologies into existing software development workflows. Key findings include a significant reduction in code error rates and a notable shortening of project completion times. The study highlights the importance of addressing challenges such as data dependency, computational resource demands, and model interpretability to enhance the generalizability and practicality of deep learning solutions in software development. Future research directions include enhancing model generalizability, lowering technical barriers, improving model interpretability, and expanding interdisciplinary cooperation.This study explores the application of deep learning technologies in software development processes, focusing on automating code reviews, error prediction, and test generation to enhance code quality and development efficiency. Through empirical studies, the research compares experimental groups using deep learning tools with control groups using traditional methods in terms of code error rates and project completion times. The results demonstrate significant improvements in the experimental group, validating the effectiveness of deep learning technologies. The study also discusses potential optimization points, methodologies, and technical challenges, as well as how to integrate these technologies into existing software development workflows. Key findings include a significant reduction in code error rates and a notable shortening of project completion times. The study highlights the importance of addressing challenges such as data dependency, computational resource demands, and model interpretability to enhance the generalizability and practicality of deep learning solutions in software development. Future research directions include enhancing model generalizability, lowering technical barriers, improving model interpretability, and expanding interdisciplinary cooperation.