Cross-Language SDK Code Generation with Consistent API Naming
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing SDK translation methods require manual translation across multiple languages, leading to high labor costs, potential errors, and slow iteration due to differing interface and field naming rules, and the need for manual updates.
Innovation Solution
Utilizing a natural language generation model to translate SDK codes across languages while maintaining consistent interface naming, reducing manual labor and errors through automated translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual translation is used to translate SDK codes across multiple languages, then translation accuracy can be maintained, but labor costs increase and iteration speed decreases
Solution Approach 1:
The patent replaces the manual mechanical translation process with an automated machine translation system. The translation module automatically translates SDK code from a source language to multiple target languages, eliminating the need for manual translation while maintaining acceptable translation quality through automated linguistic processing.
Solution Approach 2:
The patent changes the parameter of translation execution from manual to automated. By implementing an automated translation module that can rapidly generate multi-language SDK codes, the system achieves both high translation accuracy and fast iteration speed, resolving the contradiction between precision and productivity.
2Reliability
If manual translation is used to adapt interface naming rules for different languages, then naming consistency can be controlled, but time consumption increases
Solution Approach 1:
The patent implements a universal translation module that handles multiple translation tasks simultaneously - translating code, adapting interface naming, and generating documentation across multiple languages. This multi-functional approach maintains naming consistency through standardized translation rules while significantly reducing time consumption compared to manual translation.
Solution Approach 2:
The system performs preliminary action by establishing a comprehensive translation rule base before actual translation occurs. The translation module is pre-configured with language-specific naming conventions and interface rules, enabling it to automatically adapt naming standards for different languages without time-consuming manual adjustments during the translation process.
3Adaptability or versatility
If different interface naming rules are applied for each language, then language-specific conventions are followed, but maintenance complexity increases
Solution Approach 1:
The patent introduces an intermediary translation module that acts as a mediator between the source code and multiple target languages. This module contains all language-specific naming rules and translation conventions, serving as a centralized intermediary that handles language adaptations automatically. This reduces maintenance complexity by centralizing all language-specific logic in one module rather than requiring separate maintenance for each language implementation.
Solution Approach 2:
The translation module serves as a universal component that handles all language-specific naming conventions through a single unified system. Rather than maintaining separate codebases for each language, the universal translation module adapts the source code to follow each language's conventions automatically, significantly reducing maintenance complexity while preserving adaptability to different language standards.
Data Source
AI summary
A code generation method and apparatus, an electronic device and a medium are provided. The method includes: acquiring a first language SDK code of a software development kit, in which the software development kit includes an application programming interface, which is configured to call a resource of an open platform; importing the first language SDK code and preset first guidance information to a natural language generation model, in which the natural language generation model is configured to generate responsive answer information according to the guidance information; and acquiring a second language SDK code output by the natural language generation model; in which the first guidance information includes a second language type and interface naming specifying information, and the interface naming specifying information is used to specify that a naming way for the application programming interface is identical in the first language SDK code and the second language SDK code.


