This study explores the application of deep learning in software development processes, focusing on automating code reviews, error prediction, and test generation to improve code quality and development efficiency. Through empirical studies, the research compares groups using deep learning tools with those using traditional methods, finding significant improvements in error rates and project completion times in the experimental group. The study discusses potential optimization points, methodologies, and technical challenges of deep learning in software development, as well as integration into existing workflows. Deep learning can enhance efficiency and software quality by automating tasks like code reviews and testing. For example, automated code review systems can identify potential errors early, reducing repair costs. Deep learning also helps predict project costs and timelines, optimizing resource allocation. The study aims to explore the feasibility and effectiveness of deep learning in optimizing software development processes through systematic analysis and empirical research. Literature review shows that deep learning has been applied in software development for automated code reviews, test case generation, and other areas. It has demonstrated its ability to handle complex problems, inspiring its application in software development. However, challenges such as data dependency, model complexity, and computational resource demands remain. Theoretical analysis shows that deep learning can enhance requirement analysis by analyzing user feedback and historical data, and improve design processes by generating design documents and architectural diagrams. Appropriate neural network models are crucial for different stages of software development. Data preprocessing and feature engineering are essential for effective deep learning applications. Empirical research involved comparing two groups: one using deep learning tools and the other using traditional methods. The experimental group showed a significant reduction in code error rates and shorter project completion times. Statistical analysis confirmed these results, supporting the hypothesis that deep learning improves software development processes. The study concludes that deep learning significantly enhances code quality and development efficiency. Future research should focus on improving model generalizability, lowering technical barriers, enhancing model interpretability, and expanding interdisciplinary cooperation. The study highlights the potential of deep learning in software development, emphasizing the need for further research to address current challenges and expand its applications.This study explores the application of deep learning in software development processes, focusing on automating code reviews, error prediction, and test generation to improve code quality and development efficiency. Through empirical studies, the research compares groups using deep learning tools with those using traditional methods, finding significant improvements in error rates and project completion times in the experimental group. The study discusses potential optimization points, methodologies, and technical challenges of deep learning in software development, as well as integration into existing workflows. Deep learning can enhance efficiency and software quality by automating tasks like code reviews and testing. For example, automated code review systems can identify potential errors early, reducing repair costs. Deep learning also helps predict project costs and timelines, optimizing resource allocation. The study aims to explore the feasibility and effectiveness of deep learning in optimizing software development processes through systematic analysis and empirical research. Literature review shows that deep learning has been applied in software development for automated code reviews, test case generation, and other areas. It has demonstrated its ability to handle complex problems, inspiring its application in software development. However, challenges such as data dependency, model complexity, and computational resource demands remain. Theoretical analysis shows that deep learning can enhance requirement analysis by analyzing user feedback and historical data, and improve design processes by generating design documents and architectural diagrams. Appropriate neural network models are crucial for different stages of software development. Data preprocessing and feature engineering are essential for effective deep learning applications. Empirical research involved comparing two groups: one using deep learning tools and the other using traditional methods. The experimental group showed a significant reduction in code error rates and shorter project completion times. Statistical analysis confirmed these results, supporting the hypothesis that deep learning improves software development processes. The study concludes that deep learning significantly enhances code quality and development efficiency. Future research should focus on improving model generalizability, lowering technical barriers, enhancing model interpretability, and expanding interdisciplinary cooperation. The study highlights the potential of deep learning in software development, emphasizing the need for further research to address current challenges and expand its applications.