Automated Error Resolution in Software Deployment Pipelines
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Solution Overview
Problem
Software deployment pipelines often encounter errors due to missed configurations, omitted steps, or periodic updates, which can delay releases and increase the workload for DevOps personnel, with some errors requiring human intervention while others can be programmatically resolved.
Innovation Solution
A method that utilizes natural language processing models to identify and automatically execute error resolution scripts from an error database, parsing job logs to characterize errors and classify them based on error classes, allowing for automated resolution of errors associated with specific classes and manual intervention for others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated error resolution scripts are implemented, then productivity and error resolution speed are improved, but device complexity and system complexity increase
Solution Approach 1:
The patent introduces an intermediary error resolution script that acts as a mediator between the pipeline error and the resolution action. The script serves as a reusable component that encapsulates the resolution logic, making the system more manageable despite increased automation complexity. This intermediary layer allows errors to be resolved automatically without directly embedding complex resolution logic throughout the pipeline system.
Solution Approach 2:
The patent segments the error resolution process into distinct components: error detection, error classification, script selection, and script execution. By dividing the resolution mechanism into separate error resolution scripts that can be independently developed and maintained, the system achieves high productivity while managing complexity through modular design. Each script handles specific error types, allowing for targeted improvements without system-wide complexity increases.
2Measurement precision
If comprehensive error databases are created, then measurement precision and error identification accuracy are improved, but loss of information and data management complexity increase
Solution Approach 1:
The patent creates simplified copies of error information in the error database, storing only essential characteristics needed for identification and resolution. Rather than maintaining complete and exhaustive error data, the system uses representative error descriptions and classifications that capture the essential patterns. This copying approach enables accurate error identification while reducing data management complexity by storing only the most relevant error attributes.
3Ease of operation
If natural language processing models are used, then ease of operation and error parsing capability are improved, but use of energy and computational resources increase
Solution Approach 1:
The patent applies partial NLP processing by focusing only on the most critical aspects of error log analysis. Rather than performing comprehensive natural language understanding on entire logs, the system uses targeted NLP techniques to extract specific error patterns and characteristics needed for classification. This partial action approach maintains ease of operation and effective error parsing while significantly reducing computational resource consumption compared to full NLP processing.
Data Source
AI summary
Techniques are provided for automated resolution of one or more pipeline errors. One method comprises obtaining information characterizing errors in a pipeline job of a software deployment pipeline; processing at least a portion of the information using a natural language processing model to identify an error resolution script that automatically addresses the errors in the pipeline job; and automatically initiating an execution of processing steps associated with the identified error resolution script to address the errors in the pipeline job. The information characterizing the errors in the pipeline job may be obtained by parsing error information in a job log. An error database may record a description of historical errors and a corresponding error resolution script. The natural language processing model may utilize information in the error database to identify an error resolution script that addresses a given error in a pipeline job.