CLI Command Recovery Using Telemetry-Trained Machine Learning
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Solution Overview
Problem
Developers face challenges in identifying correct CLI commands and parameter values due to dynamic syntax changes and limited reference documentation, leading to command failures and a lack of effective guidance in cloud-based applications.
Innovation Solution
A failure recommendation system using machine learning models, including a conditional probability model, failure type classifier, and parameter value type classifier, to predict and recommend commands likely to succeed in correcting CLI failures based on historical usage data and publicly accessible sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If developers manually search for CLI command documentation and troubleshoot failures, then they can find correct commands, but this process is time-consuming and reduces productivity
Solution Approach 1:
The system implements feedback by analyzing CLI telemetry data from successful and failed command executions, using this feedback to train machine learning models that predict correct commands and parameter values, thereby continuously improving accuracy while reducing manual troubleshooting time
Solution Approach 2:
The patent replaces the mechanical process of manual documentation search and trial-and-error troubleshooting with an automated machine learning-based recommendation system that predicts correct CLI commands and parameters, significantly improving both accuracy and developer productivity
2Adaptability or versatility
If CLI syntax changes dynamically to accommodate new features, then the system remains adaptable, but reference documentation becomes outdated and less reliable
Solution Approach 1:
The system performs preliminary action by proactively predicting correct commands and parameter values based on patterns learned from historical telemetry data, providing recommendations before developers encounter syntax issues, thus maintaining reliability despite dynamic syntax changes
Solution Approach 2:
The patent implements self-service by enabling the CLI system to automatically learn from its own usage patterns and telemetry data, generating up-to-date command recommendations without relying on external documentation that may become outdated
3Ease of operation
If comprehensive reference documentation is provided for all CLI commands and parameters, then users can find correct syntax, but the system complexity and resource requirements increase
Solution Approach 1:
The system applies local quality by providing context-specific command recommendations tailored to the user's current situation and error state, rather than presenting all possible commands, thereby improving ease of operation without proportionally increasing system complexity
4Difficulty of detecting and measuring
If detailed error messages are provided for failed commands, then users can understand what went wrong, but the lack of guidance on corrections reduces ease of operation
Solution Approach 1:
The system uses feedback from error messages combined with historical telemetry data to not only detect failures accurately but also generate predictive recommendations for correct commands, thereby improving ease of operation while maintaining accurate failure detection
Data Source
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AI summary
A failure recommendation system for a command line interface (CLI) uses machine learning to predict the most likely command to correct an unsuccessful or failed attempt to perform an intended operation using the CLI. The failure recommendation system is based on a conditional probability model trained on failure-success pairs of commands from CLI telemetry data to learn the most likely command to remediate a failure. The conditional probability model predicts the most likely command based on a failure type and the failed command. The failure type is identified through a failure type classifier and is used to select the most likely command to remediate a failure from the different events that may lead to a failure.