AI Virtual Programmer for Automated Code Generation
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Solution Overview
Problem
The manual process of software development is repetitive and time-consuming, requiring developers to understand and translate requirements from natural human language to programming language, and existing technologies do not efficiently automate this process.
Innovation Solution
An AI Virtual Programmer that understands natural human language and various programming languages, using machine learning and neural networks to generate computer programs autonomously, reducing the need for manual translation and improving productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual software development process is used, then developers can understand and translate requirements to programming language, but the process is repetitive and time-consuming
Solution Approach 1:
The system enables self-service by allowing the AI model to automatically generate code from natural language requirements without continuous human intervention. The model learns from training data and autonomously translates requirements into programming code, reducing repetitive manual translation work and significantly decreasing the time required for software development tasks.
2Productivity
If AI Virtual Programmer is used to autonomously generate code, then productivity is enhanced, but system complexity increases
Solution Approach 1:
The system uses natural language as an intermediary between human requirements and programming code. The AI model processes natural language input, understands the requirements, and generates appropriate code output. This intermediary approach simplifies the interaction interface while managing the underlying complexity of the AI system through standardized processing pipelines and training frameworks.
Data Source
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AI summary
Systems and methods of generating a computer program using artificial intelligence module include generating logic programming by analyzing natural language in sample input data received from an external source, the sample input data resulting in a known output. Select input data, which includes select natural language or a coding instruction including the select natural language, is received. Context data is generated by processing the select natural language. The logic programming based on the context data is selected. A computing instruction is determined for the select input data using the logic programming, and the computer program including the computing instruction is generated.