Application Engineering Platform Automating API Selection
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Solution Overview
Problem
Existing application engineering platforms face challenges in efficiently selecting appropriate application programming interfaces (APIs) and user interfaces (UIs) for application development, leading to inefficiencies and potential errors due to the complexity of vast API repositories and the need for specialized domain skills.
Innovation Solution
An application engineering platform (AEP) that utilizes artificial intelligence techniques, such as natural language processing and machine learning, to automatically select APIs and UIs based on user input, generating application code and deploying it to a cloud environment, thereby reducing manual effort and human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection of APIs and UIs is performed by developers, then specialized domain skills can be applied to ensure correctness, but the process becomes time-consuming and error-prone due to the vast complexity of API repositories
Solution Approach 1:
The system enables self-service by allowing the API selection process to occur automatically without human intervention. The natural language processing system autonomously interprets requirements and selects appropriate APIs and UIs, eliminating the time-consuming manual search while maintaining reliability through AI-driven accuracy.
Solution Approach 2:
The patent introduces an intermediary natural language processing system that mediates between the developer's requirements and the vast API repository. This intermediary translates human language into precise API selections, bridging the gap between domain knowledge requirements and the complexity of API selection, thereby reducing both time and errors.
2Productivity
If automated API selection is implemented using AI techniques, then time and manual effort are reduced, but the system complexity increases due to integration of NLP and machine learning components
Solution Approach 1:
The natural language processing system serves multiple functions: it interprets requirements, selects APIs, chooses UIs, and generates code. This multi-functionality consolidates what would otherwise require separate systems into a single unified platform, increasing productivity while managing complexity through integration rather than proliferation of components.
Solution Approach 2:
The patent merges previously separate functions (requirement analysis, API selection, UI selection, and code generation) into a single integrated natural language processing system. This combining approach streamlines the development process and improves productivity while containing system complexity through unified architecture.
3Measurement precision
If comprehensive API repositories are maintained to cover all possible application features, then selection accuracy improves, but the repository size and maintenance burden increase significantly
Solution Approach 1:
The patent replaces the mechanical approach of manually curating and searching through vast API repositories with an automated natural language processing system. This substitution maintains high measurement precision in feature matching while dramatically reducing the effective repository size needed, as the AI system can accurately match features without requiring exhaustive manual curation of every possible API.
Data Source
AI summary
An example device may include one or more processors to receive an input associated with developing an application; determine a feature that may be included in the application based on the input; select an application programming interface (API) from an API repository, where the API may be associated with the feature of the application; select a user interface (UI) to facilitate user interaction with the application based on the API; and/or perform an action associated with developing the application.