Prompt-Driven Code Module Regeneration for Maintainable Development
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional software and hardware development methods rely on manually written code, which can be time-consuming and error-prone, leading to complex, interwoven code structures that are difficult to understand and modify, and AI-based tools primarily focus on localized changes, exacerbating maintenance challenges.
Innovation Solution
Utilizing natural language prompts as primary development artifacts, generating and verifying code modules through large language models (LLMs) and other generative models, with a system that includes management, generation, verification, and update engines to ensure code meets requirements and constraints, reducing complexity and overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manually written code is used as the primary artifact for software and hardware development, then developers can have full control over the code structure and functionality, but the development process becomes time-consuming and error-prone, leading to complex code structures that are difficult to understand and modify
Solution Approach 1:
The patent introduces natural language prompts as an intermediary between developer intent and code implementation. Instead of directly writing complex code, developers write simple natural language prompts that are then translated into code by AI models. This intermediary layer simplifies the development process while maintaining code quality and reducing complexity accumulation.
Solution Approach 2:
The patent replaces the mechanical process of manual code writing and modification with an AI-based generative system. Instead of manually editing code to add features or fix bugs, developers use natural language prompts to generate or modify code automatically. This substitution dramatically improves productivity while the AI system maintains code structure integrity.
2Ease of repair
If patches are applied to existing codebases to modify functionality or fix bugs, then specific issues can be addressed, but the accumulation of patches increases code complexity and makes the codebase increasingly difficult to understand and modify
Solution Approach 1:
The patent applies preliminary action by generating complete, refined code modules in advance through AI models rather than accumulating incremental patches. When functionality needs to be added or bugs fixed, the AI system generates the complete corrected or enhanced code based on natural language prompts, avoiding the gradual complexity accumulation that occurs with traditional patching.
Solution Approach 2:
The patent changes the fundamental parameter of code modification from incremental patching to holistic regeneration. Instead of applying small changes that accumulate complexity, the system regenerates complete code modules or functions based on updated natural language prompts, effectively resetting the complexity parameter while maintaining functionality.
3Productivity
If AI-based tools are used to provide real-time suggestions and auto-complete functionality, then coding efficiency is improved, but these tools focus on localized changes that contribute to complexity accumulation and maintenance challenges
Solution Approach 1:
The patent inverts the traditional AI-assisted coding approach. Instead of having AI provide small localized suggestions that accumulate changes, the system uses natural language prompts to generate complete, standalone code modules or functions. This inversion allows for discrete, self-contained code units that are easier to maintain and less prone to complexity accumulation.
Data Source
AI summary
The disclosed embodiments provide a technique for performing prompt-driven code generation and development. The technique includes determining a first version of a first prompt that is associated with a set of requirements for a system. The technique also includes generating, via execution of one or more machine learning models based on the first version of the first prompt, (i) a first code module associated with the system, (ii) a usage example associated with the first code module, and (iii) one or more tests of the code module. The technique further includes determining a second version of the first prompt based on (i) the first version of the first prompt and (ii) one or more results of the one or more tests and generating, via execution of the machine learning model(s) based on the second version of the first prompt, a second code module associated with the system.