Neural CLI Example Generation With Command Validation
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Solution Overview
Problem
Command line interfaces (CLI) require users to be familiar with numerous commands, sub-commands, and parameters, often with limited or outdated documentation, making it difficult to execute operations correctly.
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 improve efficiency and speed over GUI, then productivity increases, but the complexity of learning and using commands increases
Solution Approach 1:
The patent introduces an intermediary system consisting of a neural transformer model and command validator that acts as a bridge between the user and the complex CLI command structure. This intermediary automatically generates syntactically correct command examples based on user intent, reducing the cognitive load on users while maintaining the efficiency benefits of CLI.
Solution Approach 2:
The system enables self-service by allowing users to obtain command examples through simple queries without needing to manually study documentation or remember syntax. The automated example generation tool serves itself by learning from command patterns and providing assistance on-demand.
2Ease of operation
If comprehensive command documentation is provided to improve ease of use, then ease of operation increases, but the quantity of documentation and system complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for command usage from the comprehensive documentation. Instead of presenting the full complex documentation structure, the system extracts and generates specific command examples tailored to user needs, eliminating unnecessary documentation overhead.
Solution Approach 2:
The system dynamically changes the parameters of documentation delivery based on user queries. Rather than providing static comprehensive documentation, the example generation tool adapts the documentation content, format, and detail level to match the specific command and user context.
3Manufacturing precision
If manual typing of commands is required to maintain precision and control, then manufacturing precision increases, but the time required for command entry increases
Solution Approach 1:
The system performs preliminary action by pre-generating syntactically correct command examples before the user needs to execute them. The neural transformer model prepares accurate command structures in advance based on user intent, so users don't need to spend time manually typing and correcting syntax errors.
Solution Approach 2:
The system creates copies of correct command patterns from the template database and modifies them for specific user needs. Instead of requiring users to type commands from scratch, the system copies proven correct command structures and adapts them, ensuring syntax accuracy while reducing entry time.
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.