AI Pipeline Failure Mitigation for Code Merge Deployment
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Solution Overview
Problem
Software deployment pipelines often experience failures due to issues like typographical errors or omitted steps, leading to delays and increased workload for DevOps personnel, as developers manually diagnose and resolve these failures, which is inefficient and time-consuming.
Innovation Solution
Implementing generative AI-based techniques to predict pipeline failures and provide reasons for such failures, along with corresponding mitigation actions, automating the resolution process to reduce manual intervention and improve pipeline quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual diagnosis and resolution of pipeline failures is performed by developers, then flexibility and adaptability in handling diverse failure scenarios is maintained, but time consumption and workload increase significantly
Solution Approach 1:
The system enables self-service by automatically diagnosing pipeline failures and generating resolution actions without requiring manual developer intervention. The AI model analyzes failure logs, identifies root causes, and proposes mitigation actions autonomously, allowing the system to resolve its own issues.
Solution Approach 2:
The patent replaces the mechanical manual process of failure diagnosis and resolution with an automated AI-based system. The classification model and generative AI model substitute human developers' analytical and problem-solving activities, transforming manual operations into automated intelligent processing.
2Reliability
If automated failure prediction and mitigation is implemented using AI models, then pipeline reliability and productivity are improved, but system complexity increases
Solution Approach 1:
The system segments the failure mitigation process into distinct functional modules: a classification model for predicting pipeline failures, a generative AI model for analyzing failure reasons, and an automated action generation component. This segmentation allows each module to specialize in a specific task, improving overall reliability while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces AI models as intermediary components between the pipeline execution environment and the failure resolution process. These intermediary models analyze pipeline logs, predict failures, and generate mitigation actions, serving as a bridge that translates raw pipeline data into actionable insights without requiring direct human intervention.
3Productivity
If developers focus on development work rather than failure fixing, then software delivery speed and productivity increase, but requires automated failure management systems
Solution Approach 1:
The automated failure management system operates autonomously to predict, analyze, and mitigate pipeline failures without requiring developer involvement. This self-service capability frees developers to focus exclusively on development work, increasing productivity while the system handles operational issues independently.
Solution Approach 2:
The system performs preliminary failure prediction and analysis before failures impact production, using AI models to identify potential issues in advance. By taking preliminary action to detect and prepare mitigation strategies for predicted failures, the system prevents disruptions and maintains high productivity without requiring reactive developer intervention.
Data Source
AI summary
Techniques are provided for failure mitigation in software deployment pipelines using generative artificial intelligence (AI). One method comprises obtaining a request to merge code changes associated with a first branch of software code with a second branch of the software code; in response to the request: obtaining information characterizing the software deployment pipeline; applying at least a portion of the information to a classification model to obtain a prediction that an implementation of the request will result in a failure; applying, in response to the prediction that the implementation of the request will result in the failure, at least a portion of the information to a generative AI model, with failure information characterizing reasons for the failure, to obtain mitigation actions to mitigate the failure; and automatically initiating processing steps associated with at least one of the mitigation actions to mitigate the failure.