IN-202541029198-A - SELF-HEALING AUTOMATION FRAMEWORK FOR CONTINUOUS INTEGRATION AND DEPLOYMENT (CI/CD)

Abstract

The present invention relates to a Self-Healing Automation Framework designed to enhance the efficiency, resilience, and reliability of Continuous Integration and Continuous Deployment (CI/CD) pipelines. The invention integrates Artificial Intelligence (AI), Machine Learning (ML), and predictive analytics to autonomously detect, diagnose, and remediate failures in software deployment workflows, reducing manual intervention and ensuring seamless software delivery. A key aspect of the invention is its real-time anomaly detection mechanism, which continuously monitors logs, system performance metrics, and error patterns to identify unexpected deviations. Leveraging AI-driven models, the framework classifies errors, performs automated root cause analysis, and dynamically triggers corrective actions based on the severity and context of the failure. Another innovative feature is the self-adaptive remediation system, which autonomously selects and executes the most suitable recovery strategy. Depending on the failure type, the system may rerun failed test cases, roll back deployments, reconfigure environments, or reallocate computing resources to restore stability. This intelligent approach ensures uninterrupted software delivery and resilience against infrastructure failures. Additionally, the invention incorporates a predictive failure prevention module, which uses ML-based trend analysis to anticipate potential failures before they occur. By analyzing historical failure data and real-time system behavior, the framework proactively adjusts configurations and deployment parameters to mitigate risks, reducing downtime and improving system reliability. Designed for compatibility with modern DevOps environments, the framework seamlessly integrates with existing CI/CD tools, cloud platforms, container orchestration systems, and microservices-based architectures. Its modular and scalable design allows deployment across diverse infrastructures, including cloud-native, hybrid, and on-premise environments. By implementing this Self-Healing Automation Framework, organizations can achieve higher deployment success rates, reduced operational overhead, and accelerated release cycles. The invention revolutionizes software delivery by introducing intelligent, autonomous, and predictive automation capabilities, setting a new benchmark in CI/CD pipeline optimization and resilience.

Inventors

• SWATHI CHUNDRU
• MAYURI ROYAL HEIGHTS
• PLOT NUMBER 810 & 831
• VENKATA RAMANA COLONY
• GOKUL PLOTS
• KUKATPALLY
• HYDERABAD
• TELANGANA 500085 INDIA

Dates

Publication Date

20250425

Application Date

20250327

Priority Date

20250327

Claims (10)

1. CLAIM: 1. A Self-Healing Automation Framework for Continuous Integration and Continuous Deployment (CI/CD), comprising an AI-driven system that autonomously detects, diagnoses, and remediates failures in software deployment workflows, reducing manual intervention and ensuring uninterrupted operations.
2. 2. The framework of claim 1, wherein an anomaly detection module continuously monitors logs, performance metrics, and error patterns to identify deviations from expected system behavior, utilizing machine learning models for real-time failure detection.
3. 3. The framework of claim 2, wherein an automated root cause analysis engine leverages AI algorithms trained on historical failure data to determine the cause of deployment or testing errors and suggest optimal corrective actions.
4. 4. The framework of claim 3, wherein a self-adaptive remediation system autonomously selects the most effective recovery strategy, including rerunning failed test cases, rolling back faulty deployments, reallocating computing resources, or modifying configurations to restore pipeline stability.
5. 5. The framework of claim 4, wherein a predictive failure prevention mechanism analyzes historical deployment data and system performance trends using machine learning to forecast potential failures and take preventive measures.
6. 6. The framework of claim 5, wherein the system dynamically adjusts CI/CD pipeline configurations based on real-time infrastructure conditions, ensuring adaptive deployment resilience across cloud, hybrid, and on-premise environments.
7. 7. The framework of claim 6, wherein a context-aware decision-making module evaluates multiple failure parameters before executing a remediation action, ensuring minimal disruption to ongoing software releases.
8. 8. The framework of claim 7, wherein the system seamlessly integrates with existing DevOps toolchains, container orchestration platforms, and microservices-based architectures, ensuring broad compatibility without disrupting current workflows.
9. 9. The framework of claim 8, wherein an AI-powered feedback loop continuously refines failure detection and remediation models by learning from past incidents, improving accuracy and efficiency over time.
10. 10. The framework of claim 9, wherein a real-time reporting and analytics dashboard provides insights into system health, failure trends, and remediation effectiveness, enabling proactive decision-making and continuous pipeline optimization.

Description

Complete SpecificationDescription:The following specification particularly describes the nature of the invention and the manner in which it is performed: FIELD OF THE INVENTION: The present invention relates to the field of software automation, particularly within the domain of Continuous Integration and Continuous Deployment (CI/CD). More specifically, the invention introduces a Self-Healing Automation Framework designed to enhance the resilience and efficiency of CI/CD pipelines. This framework integrates Artificial Intelligence (AI), Machine Learning (ML), and intelligent automation to proactively detect, diagnose, and autonomously resolve failures encountered in software deployment environments. Modern CI/CD pipelines enable rapid software delivery but are prone to failures caused by network instabilities, infrastructure issues, configuration mismatches, and software defects. Traditional automation frameworks lack intelligent recovery mechanisms, requiring manual intervention and causing delays. This invention addresses these challenges by introducing a self-healing mechanism that detects and rectifies pipeline failures in real time, ensuring minimal disruptions. A novel aspect of this invention is its ability to perform intelligent root cause analysis and dynamically optimize recovery strategies. By continuously monitoring logs, error messages, and performance metrics, the framework classifies failures, correlates them with historical incidents, and applies optimal remediation strategies. This adaptive approach minimizes downtime and enhances system reliability. Another key feature of the self-healing automation framework is proactive failure prevention. By utilizing predictive analytics, the system forecasts potential failures based on system performance trends and past incidents. It preemptively adjusts configurations, reallocates resources, and stabilizes environments to prevent failures before they occur, improving the overall robustness of the CI/CD pipeline. The framework integrates seamlessly with existing CI/CD tools and DevOps ecosystems, ensuring broad applicability without major infrastructure modifications. By minimizing deployment risks and optimizing system reliability, this invention enables uninterrupted software delivery cycles and reduces operational overhead through intelligent automation and autonomous recovery mechanisms. BACKGROUND OF THE PROPOSED INVENTION: Continuous Integration and Continuous Deployment (CI/CD) pipelines have become essential in modern software development, enabling rapid and automated code integration, testing, and deployment. However, these pipelines are prone to failures caused by infrastructure issues, software bugs, configuration errors, and environmental inconsistencies. Such failures often require manual intervention, leading to downtime, deployment delays, and increased operational costs. Traditional CI/CD frameworks rely on static recovery mechanisms that lack adaptability to dynamic failures. These systems do not possess self-healing capabilities, making them inefficient in handling unpredictable disruptions. As software architectures evolve with cloud-native environments, microservices, and containerized applications, the need for an intelligent, autonomous recovery mechanism becomes increasingly critical. The proposed Self-Healing Automation Framework introduces an AI-driven, adaptive approach to CI/CD pipeline automation. Unlike conventional frameworks, it proactively monitors the pipeline, detects anomalies in real time, performs root cause analysis, and applies automated remediation strategies without human intervention. The framework learns from past failures using machine learning models, continuously refining its detection algorithms and optimizing recovery processes. A key innovation of this invention is its context-aware recovery mechanism, which dynamically selects the most effective remediation approach based on the specific failure scenario. This may involve rerunning failed test cases, rolling back deployments, reallocating resources, or reconfiguring environments to ensure successful execution. Additionally, predictive analytics help forecast potential failures, enabling preventive measures that reduce downtime and improve system reliability. Seamlessly integrating with existing DevOps toolchains and cloud platforms, this framework enhances CI/CD resilience by reducing manual troubleshooting, accelerating deployment cycles, and optimizing resource utilization. By implementing this self-healing automation framework, organizations can achieve a more stable, efficient, and autonomous CI/CD pipeline, setting a new benchmark for intelligent software delivery automation. SUMMARY OF THE PROPOSED INVENTION: The present invention introduces a Self-Healing Automation Framework for Continuous Integration and Continuous Deployment (CI/CD) pipelines. This novel framework enhances the reliability and efficiency of software delivery by integrating A