Design and implementation of a software development system based on sequence optimization and improved fuzzy algorithm

Abstract While traditional development methods struggle to handle the dynamic requirements and resource limitations of modern software systems, existing intelligent scheduling models often fail to effectively integrate task dependency management with real-time uncertainty processing. To address this primary research gap, this paper proposes an integrated development system based on sequence optimization and an improved fuzzy algorithm. The system uses a dynamic programming sequence-optimization model to handle task dependencies and combines parameter-adaptive fuzzy reasoning to address uncertainties in the development process. Experiments were conducted on a synthetic dataset containing 10,000 samples. The results show that compared with traditional sequence optimization methods, the average task completion time is reduced by 40% to 65%; compared with the standard fuzzy decision-making model, the defect detection rate is increased to 94%; the resource utilization rate reaches 95%, and the plan stability index reaches 0.91. In particular, in large-scale projects, the algorithm’s convergence speed improves by more than 30%, while maintaining stability above 0.92 in a high-noise environment. The research indicates that this system can effectively improve the efficiency and quality of software development and provide a new path for intelligent development.