A Deep Learning-Based Platform Engineering Framework for Predictive CI/CD Pipeline Optimization and Developer Productivity Enhancement
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P122Keywords:
CI/CD Pipelines, Deep Learning, Platform Engineering, DevOps Optimization, Predictive Analytics, Developer Productivity, LSTM, Transformer Models, Pipeline Automation, Software Engineering IntelligenceAbstract
Continuous Integration and Continuous Deployment (CI/CD) pipelines are foundational to modern software engineering practices, enabling rapid delivery, automation, and quality assurance. However, increasing system complexity, heterogeneous toolchains, and scaling challenges often lead to inefficiencies such as pipeline delays, build failures, resource underutilization, and developer productivity bottlenecks. Traditional rule-based monitoring and reactive optimization techniques are insufficient to address the dynamic and non-linear nature of modern DevOps ecosystems. This paper presents a Deep Learning-Based Platform Engineering Framework designed to enable predictive optimization of CI/CD pipelines while simultaneously enhancing developer productivity. The proposed framework integrates deep neural architectures with platform engineering principles to provide proactive insights, automated decision-making, and adaptive pipeline configurations. By leveraging historical pipeline execution data, system telemetry, and developer interaction patterns, the framework predicts pipeline failures, execution time anomalies, and resource bottlenecks with high accuracy. The architecture consists of four core layers: (1) Data Acquisition Layer, which collects logs, metrics, and developer activity data; (2) Feature Engineering Layer, which transforms raw data into structured representations; (3) Deep Learning Prediction Engine, incorporating models such as LSTM, Transformer networks, and Graph Neural Networks (GNNs) to capture temporal and dependency relationships; and (4) Optimization and Feedback Layer, which dynamically adjusts pipeline parameters and provides actionable recommendations. A key contribution of this work is the integration of platform engineering practices, enabling reusable, standardized CI/CD modules and self-service infrastructure. This reduces cognitive load on developers while ensuring consistency and scalability. The framework also introduces a novel Predictive Pipeline Efficiency Score (PPES), which quantifies pipeline performance based on latency, reliability, and resource utilization. Experimental evaluation demonstrates significant improvements in pipeline efficiency, including reductions in build time, failure rates, and resource consumption. Additionally, developer productivity metrics such as cycle time, deployment frequency, and mean time to recovery (MTTR) show measurable enhancement. The results validate the effectiveness of deep learning in capturing complex pipeline behaviors and enabling proactive optimization strategies. This study contributes to the intersection of DevOps, AI-driven software engineering, and platform engineering by providing a scalable, intelligent framework that bridges operational efficiency and developer experience. The proposed approach has practical implications for organizations seeking to modernize their CI/CD systems and adopt intelligent automation for sustainable software delivery.
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