ML Code Generation with Human Intervention and Validation

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Solution Overview

Problem

Software development is hindered by inherent limitations and risks associated with employing human developers, particularly in generating and testing source code, which can be inefficient and labor-intensive.

Innovation Solution

A system and method utilizing machine learning models, specifically large language models, to programmatically generate source code by constructing prompts based on project configuration information and intervention requests, with human input integrated through digital communications, and validating the generated code through automated and manual testing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If human developers are employed for software development, then creativity and problem-solving capabilities are maintained, but labor intensity and development time increase

Engineering Contradiction:

Improvesoftware development efficiencyVSAvoiddevelopment time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces human developers with an automated machine learning-based system that generates, tests, and validates software code autonomously. The system uses prompts to generate code, runs automated tests, and provides feedback loops to refine outputs, eliminating the need for manual coding and testing operations while maintaining development productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-validation by automatically executing test cases against generated code and using the results to refine subsequent code generation. The feedback mechanism allows the system to self-correct and improve its outputs without human intervention, enabling autonomous software development cycles.

Inventive Principle:

Principle #25Self-service

2Productivity

If automated code generation is implemented, then development speed increases, but code quality and validation reliability may deteriorate

Engineering Contradiction:

Improvecode generation speedVSAvoidcode quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system incorporates a feedback loop where automated test results are fed back into the code generation process. Test failures and performance metrics are used to refine subsequent code generation prompts, ensuring that only validated code is produced and continuously improving code quality through iterative feedback.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary testing and validation before final code deployment. Test cases are executed in advance to verify code functionality, and only code that passes validation is considered complete, ensuring quality assurance is built into the generation process rather than added as a subsequent step.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240427566A1Systems and Methods for Software Development Using Machine Learning Models

Publication Date: 2024.12.26 PROVOKE SOLUTIONS LLC

AI summary

Systems and methods for software development using machine learning models are disclosed. In one embodiment, a method for programmatically generating source code using a machine learning model includes obtaining a set of project configuration information that defines a programming task, indexing original source code in a code repository, advancing the programming task by constructing a prompt to a machine learning model to obtain new source code as an output, writing the new source code to the code repository, identifying an intervention request that requires human input to complete the programming task, obtaining human input for the intervention request from a human using digital communications as an outcome, and writing the intervention request and outcome to an intervention database.