AI-Augmented Continuous Integration for Dynamic Resource Allocation

As the market for software development continues to grow and become increasingly saturated, integrating artificial intelligence into the continuous integration process provides a huge opportunity to optimize cloud resources. This paper discusses the development of AI models that can predict and further adjust the cloud resource in the CI phase based on the historical pipeline performance data and workload trends. With the help of LSTM networks and RL algorithms, the proposed models can optimize resource utilization, decrease costs, and avoid over-provisioning. The mentioned approach implies data gathering and preparation, model training, and Integration with other tools of CI/CD processes. The evaluation shows that resource utilization has been optimized while the number of idle resources has decreased, and resource costs are lower than other resource allocation methods. In addition, the DevOps teams' feedback reveals improved confidence in the resource management decision-making based on the AI-derived data. This work also highlights how incorporating AI into CI can enhance the management of cloud resources to enhance software development productivity. The future research directions are as follows: Model interpretability, standard integration frameworks, and ethical issues in AI-based resource allocation.