Machine-Generated CLI Command Examples Using ML Parameter Prediction
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Solution Overview
Problem
Command line interfaces (CLI) face challenges due to limited and outdated documentation, requiring users to be familiar with numerous commands and their syntax, which can lead to errors and inefficiencies, especially with complex combinations of parameters and parameter values.
Innovation Solution
A machine learning model is used to predict the data type of parameter values, selecting appropriate values from known options or generating new ones based on telemetry and web-based usage examples, enabling the generation of real-time, updated documentation and usage examples for CLI commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If human-generated examples are used for CLI documentation, then documentation can be created, but the coverage of commands and functions remains limited
Solution Approach 1:
The system enables self-service by automatically generating documentation examples using machine learning models that analyze telemetry data and command syntax, eliminating the need for manual human generation of usage examples for each command
Solution Approach 2:
The patent replaces the mechanical process of manual example creation with an automated machine learning system that predicts parameter values and generates usage examples based on patterns learned from telemetry data and command syntax analysis
2Ease of operation
If comprehensive usage examples are generated for all command combinations, then user assistance improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of generating comprehensive documentation into manageable components: collecting telemetry data, analyzing command syntax, training separate machine learning models for different parameter types, and generating examples through a pipeline that processes commands incrementally
Solution Approach 2:
The patent introduces intermediary machine learning models that act as mediators between the raw telemetry data and the final usage examples, translating complex patterns into predicted parameter values and structured documentation content
3Measurement precision
If machine learning models predict parameter values, then accurate usage examples are generated, but computational resources increase
Solution Approach 1:
The system performs preliminary action by collecting and storing telemetry data over time before generating documentation, and by pre-training machine learning models on historical data, so that actual documentation generation requires only inference rather than full model training
Solution Approach 2:
The patent employs lightweight machine learning models that can be quickly trained and discarded, replacing expensive long-term model maintenance with efficient retraining on updated telemetry data, reducing overall computational burden
Data Source
AI summary
Examples of the usage of a command of a command line interface includes the command with a set of parameters and corresponding parameter values. The examples are generated from telemetry data, which does not contain parameter values, and from web-based sources that may contain multiple parameter values. A machine learning model is used to predict the data type of a parameter value when the parameter is used with a particular command. The predicted data type is then used to select an appropriate parameter value for the example from multiple known parameter values or to generate a parameter value when no known parameter value exists.