Code Generation from Docstrings via ML Verification

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional methods for writing or understanding computer code lack integration with natural language processing models, requiring extensive knowledge of programming languages and syntax, and fail to understand context, leading to inefficiencies and errors in code generation and management.

Innovation Solution

A method using a trained machine-learning model to generate computer code from natural language input, where a docstring is used to produce candidate code samples, with execution and verification to identify correct samples, and fine-tuning based on feedback for improved accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If conventional methods for writing or understanding computer code are used, then extensive knowledge of programming languages and syntax is required, but code generation efficiency and accessibility are reduced

Engineering Contradiction:

Improvecode generation accessibilityVSAvoidprogramming knowledge requirement

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

A trained machine learning model serves as an intermediary between natural language input and computer code output. The model translates high-level natural language descriptions into syntactically correct code across multiple programming languages, eliminating the need for users to directly learn programming syntax while maintaining code generation quality

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual code writing mechanics with an automated machine learning system. Instead of requiring users to manually construct code following syntactical rules, the system automatically generates code based on natural language descriptions, substituting the mechanical process of code construction with intelligent automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If conventional code writing methods are used, then context understanding is limited, but code accuracy and tailoring to specific environments are reduced

Engineering Contradiction:

Improvecode accuracyVSAvoidcontext understanding

Core Design Contradiction:

Manufacturing precisionVSLoss of information

Solution Approach 1:

The system incorporates feedback mechanisms where the machine learning model is trained on execution results and verification outcomes. Code samples are executed and verified against expected behavior, with this feedback used to refine and improve the model's context understanding and code generation accuracy over time

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary actions by training the machine learning model on extensive code datasets and context information before actual code generation. The model pre-learns programming patterns, syntax rules, and contextual relationships, enabling it to generate accurate, context-appropriate code without requiring real-time analysis

Inventive Principle:

Principle #10Preliminary action

3Reliability

If manual code verification and testing are performed, then syntax errors and bugs can be detected, but time consumption and development complexity increase

Engineering Contradiction:

Improvecode correctnessVSAvoidverification time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements self-service verification where the machine learning model generates multiple candidate code samples and automatically executes and verifies them. The model autonomously performs testing and validation without requiring manual intervention, reducing verification time while maintaining code correctness through automated functional correctness scoring

Inventive Principle:

Principle #25Self-service

4Adaptability or versatility

If code is written for specific programming languages and platforms, then code functionality is achieved, but adaptability across multiple languages and platforms is reduced

Engineering Contradiction:

Improvecross-language compatibilityVSAvoidmulti-language support complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The machine learning model is designed with multi-functionality to generate code across multiple programming languages and platforms from a single natural language input. The model learns language-specific patterns and syntax rules during training, enabling it to adaptively generate appropriate code for different target languages without requiring separate systems for each language

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12061880B2Systems and methods for generating code using language models trained on computer code

Publication Date: 2024.08.13 OPENAI OPCO LLC

AI summary

Disclosed herein are methods, systems, and computer-readable media for generating computer code based on natural language input. In an embodiment, a method may comprise one or more of: receiving a docstring representing natural language text specifying a digital programming result; generating, using a trained machine learning model, and based on the docstring, a computer code sample configured to produce respective candidate results; causing the computer code sample to be executed; identifying, based on the executing, a computer code sample configured to produce a particular candidate result associated with the digital programming result; performing at least one of outputting, via a user interface, the identified computer code sample, compiling the identified computer code sample, transmitting the identified computer code sample to a recipient device, storing the identified computer code sample, and/or re-executing the identified computer code sample.