Machine Learning Model for Automated Software Definition Generation
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Solution Overview
Problem
The complexity of software development often prevents individuals from creating software applications due to the need for specialized skills, resources, and iterative processes that consume significant computational resources.
Innovation Solution
A computer-implemented method using a machine-learned language model to generate an application definition from natural language inputs, allowing for automated software development and reducing the need for extensive coding through an application development platform that integrates with low-code or no-code tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional iterative software development processes are used, then software applications can be developed with high functionality and reliability, but significant computational resources and time are consumed
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive software development data, codebases, and documentation before actual development occurs. This pre-computed knowledge enables the system to generate functional software components directly from natural language prompts, eliminating the need for traditional iterative development cycles and significantly reducing development time while maintaining reliability through the model's trained understanding of software patterns and best practices.
2Adaptability or versatility
If traditional software development processes are used, then software applications can be developed with high functionality, but specialized skills and resources are required
Solution Approach 1:
The patent introduces an intermediary machine learning model that translates natural language requirements into functional software code. This intermediary layer acts as a mediator between the user's intent and the complex software development process, eliminating the need for users to possess specialized programming skills or understand complex development workflows, thereby making software development accessible to non-experts while maintaining high functionality.
3Productivity
If automated generation of application definitions is implemented, then development speed and accessibility are improved, but the complexity of the development system increases
Solution Approach 1:
The patent employs copying by training the machine learning model on extensive existing software codebases, patterns, and best practices. The model learns to replicate proven software development patterns and architectural solutions rather than generating code from scratch. This approach enables rapid generation of high-quality software while the system complexity is managed through the use of pre-learned patterns rather than requiring complex real-time decision-making infrastructure.
Data Source
AI summary
Provided are systems and methods that leverage a machine-learned language model to perform automated generation and/or modification of an application definition for a software application based on natural language inputs. For example, the techniques can be implemented as part of or by an application development platform that enables users to develop software applications using low-code or no-code tools.