Code Pipeline Failure Diagnosis Using Execution State Comparison
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Solution Overview
Problem
Identifying the cause of failures in a code pipeline, which can occur due to issues with the code being processed or changes in the pipeline's software and configurations, is challenging, leading to inefficiencies and potential disruptions.
Innovation Solution
A fault diagnosis system that tracks information about the code pipeline's execution, including software versions, configurations, and settings, and compares successful and failed executions to determine whether the failure is caused by the code or pipeline changes, providing feedback to developers for resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated code pipeline execution is implemented, then deployment efficiency is improved, but failure diagnosis difficulty increases
Solution Approach 1:
The system performs preliminary actions by tracking and recording pipeline execution information before failures occur. It maintains a database of successful execution patterns, software versions, and configuration states, enabling rapid comparison and diagnosis when failures happen, thus resolving the contradiction between automated deployment efficiency and failure diagnosis difficulty
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring pipeline executions, comparing successful and failed runs, and providing diagnostic information back to developers. This feedback loop identifies root causes of failures by analyzing differences between successful and failed executions, maintaining high deployment efficiency while reducing diagnosis difficulty
2Measurement precision
If comprehensive pipeline tracking is implemented, then failure diagnosis accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts only the essential and relevant information needed for failure diagnosis, such as execution status, software versions, and configuration parameters. By focusing on critical data points rather than comprehensive tracking of all pipeline activities, it achieves high diagnosis accuracy while maintaining manageable system complexity
Solution Approach 2:
The tracking system is designed to be universal and multi-functional, serving both successful and failed execution analysis with the same infrastructure. This approach improves diagnosis accuracy through comprehensive data collection while avoiding the complexity increase that would result from maintaining separate specialized systems
Data Source
AI summary
In some implementations, a fault diagnosis system may track information associated with a first execution of a code pipeline to deploy an updated version of software associated with the code pipeline. The fault diagnosis system may receive an indication of a failure in a second execution of the code pipeline, wherein the second execution of the code pipeline is triggered by a commit of source code to a source code repository. The fault diagnosis system may determine whether the updated version of the software associated with the code pipeline or the source code committed to the source code repository is a cause of the failure in the second execution of the code pipeline. The fault diagnosis system may send a request to plan code development to resolve the cause of the failure in the second execution of the code pipeline.