Software Quality Evaluation Optimization Method in Continuous Integration Environment Based on Adaptive Genetic Algorithm

With the widespread use of Continuous Integration (CI) environments, frequent code changes and integrations have raised higher demands for the accuracy and stability of software quality assessment. Traditional software quality evaluation methods often rely on fixed evaluation standards, which struggle to adapt to the rapidly changing development environments. This paper proposes an optimization method for software quality assessment based on adaptive genetic algorithms. By dynamically adjusting evaluation standards and weights, the method can automatically adapt to changes in the development environment, significantly improving the accuracy and flexibility of the evaluation. Through comparative experiments with traditional methods, the results show that the optimization method based on genetic algorithms exhibits higher accuracy across multiple projects, particularly demonstrating significant improvements in defect rates and performance evaluation. This method can effectively cope with high-frequency code changes in continuous integration environments, enhancing the stability and reliability of assessments. The research presented in this paper provides an innovative solution for quality management in CI environments, further promoting the intelligent and dynamic development of quality assessment methods and offering theoretical support for future research and practical applications.