Neural CLI Example Generation for Complex Command Syntax
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Solution Overview
Problem
Command line interfaces (CLI) require users to be familiar with numerous commands and their syntax, which can be challenging due to limited or outdated documentation, especially in cloud services with vast numbers of commands and parameters.
Innovation Solution
An example generation tool using a neural transformer model with attention to predict correct parameter values for CLI commands, assisted by a command validator to ensure syntax and data format correctness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a command line interface is used to manage cloud service resources, then productivity and efficiency are improved, but the complexity of learning and operating the interface increases due to numerous commands and parameters
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between users and complex CLI commands. The assistant provides natural language explanations, generates example commands, and offers guidance to help users understand and use CLI commands without needing to memorize syntax, thereby maintaining productivity while improving ease of operation.
Solution Approach 2:
The system enables self-service by automatically generating relevant CLI command examples and explanations based on user context. The AI assistant proactively provides information and examples without requiring users to search documentation, allowing users to independently understand and execute commands efficiently.
2Loss of information
If comprehensive documentation is provided for all CLI commands and parameters, then completeness of information is improved, but the amount of information to process and remember increases
Solution Approach 1:
The patent applies local quality by providing customized, context-specific information rather than overwhelming users with comprehensive documentation. The AI assistant identifies the user's specific needs based on their current context and provides only the relevant command examples and explanations needed, reducing information overload while maintaining completeness.
Solution Approach 2:
The system uses partial action by generating only the necessary command examples relevant to the user's current context rather than providing all possible commands. This selective approach reduces the quantity of information to process while ensuring the provided information is sufficient for the user's immediate needs.
3Speed
If a neural transformer model is used to generate command examples, then the speed of example generation is improved, but the computational resources and complexity of the system increase
Solution Approach 1:
The patent implements preliminary action by pre-training the neural transformer model on comprehensive CLI command datasets before actual use. The model is pre-trained to generate accurate and relevant command examples, so that during actual operation, the system only needs to process user context and retrieve pre-learned patterns, significantly reducing real-time computational requirements while maintaining fast generation speeds.
Data Source
AI summary
An example generator tool generates an example illustrating correct usage of a command of a command line interface. A command may include a command name, zero or more subcommands, and one or more parameters with a corresponding parameter value. A template containing the correct syntax of the command is obtained from a template database. Parameter values for the template are generated from a neural transformer with attention given the command template.