ML-Based Build Block Bottleneck Detection in DevOps Pipelines
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Solution Overview
Problem
In DevOps, identifying and analyzing bottlenecks and predicting risk values in lengthy processes with complex dependencies is challenging, especially when there is a high risk of failure, and monitoring and measuring these risks is difficult.
Innovation Solution
The approach trains machine learning models to predict risk values of build blocks by analyzing DevOps pipeline statistics, identifying bottlenecks, and measuring overall DevOps risk values by defining and deploying build blocks based on pipeline statistics and bottleneck analysis, using software modules to automatically generate configuration and scripts for target offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained to predict risk values of build blocks, then the ability to identify bottlenecks and predict risks is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that mediate between build block data and risk assessment. These models process complex dependencies and pipeline statistics to generate risk predictions, acting as a bridge that transforms raw build data into actionable risk insights without requiring direct complex analysis of all build block interactions
Solution Approach 2:
The patent segments the DevOps pipeline into discrete build blocks with specific features and attributes. By dividing the complex pipeline into manageable segments (build blocks with defined features like build time, failure history, dependency count), the system can apply machine learning models to each segment independently, making the overall system more tractable despite the complexity
2Productivity
If build blocks are deployed with automatic configuration generation, then productivity is improved, but the difficulty of detecting and measuring bottlenecks increases
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor build block execution and feed performance data back to the machine learning models. This feedback loop enables the system to detect bottlenecks by analyzing patterns in build block performance metrics, automatically adjusting risk predictions and identifying problematic areas without manual intervention
Solution Approach 2:
The patent replaces manual bottleneck detection and analysis with automated machine learning-based detection. Instead of relying on manual inspection or simple monitoring tools, the system uses trained models to automatically detect and measure bottlenecks by analyzing build block features and performance data, substituting mechanical monitoring approaches with intelligent automated detection
Data Source
AI summary
An approach is provided in which the approach trains a first machine learning model using a set of features corresponding to a set of build blocks. The set of build blocks include at least one dependency build block and at least one artifact package build block. The approach predicts a set of risk values of the set of build blocks using the trained first machine learning model, and marks at least one of the build blocks as a bottleneck in response to comparing the set of risk values against a risk threshold.