Cloud API Debugging Through Dependency-Based Parameter Recommendations

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Solution Overview

Problem

Current cloud service API debugging tools lack efficient parameter selection and debugging mechanisms, leading to incomplete or inaccurate error prompts, which complicates troubleshooting and increases debugging time due to the absence of personalized and scenario-specific recommendations.

Innovation Solution

A method and apparatus that pre-extract group dependency relationships between cloud service API parameters, recommending related parameter groups and performing pre-verification to enhance parameter filling accuracy and efficiency, while providing panoramic error information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If cloud vendors provide massive APIs with full-element API information retrieval and online debugging functions, then API search and basic debugging capabilities are improved, but parameter selection accuracy, debugging efficiency, and troubleshooting completeness deteriorate due to lack of personalized and scenario-specific recommendations

Engineering Contradiction:

ImproveAPI debugging convenienceVSAvoidDebugging efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system performs preliminary extraction of group dependency relationships between parameters before the user needs to debug. It pre-organizes parameter groups and their dependencies, so when a user selects an API, the related parameter groups are already prepared and recommended, eliminating the need for users to manually search and select parameters during debugging.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides feedback by analyzing the user's parameter selection and automatically recommending related parameter groups based on the extracted dependency relationships. This feedback mechanism continues to refine recommendations based on user interactions, making the debugging process more efficient and targeted.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If cloud vendors provide general API debugging functions without scenario-specific recommendations, then basic API testing is enabled, but parameter recommendation correlation and accuracy deteriorate

Engineering Contradiction:

ImproveAPI debugging coverageVSAvoidParameter recommendation accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system segments the API parameters into distinct parameter groups based on their functional relationships and dependency structures. This segmentation allows the system to provide targeted recommendations for specific parameter groups rather than treating all parameters uniformly, thereby improving recommendation accuracy while maintaining broad API coverage.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies local quality by providing personalized parameter recommendations tailored to each specific API and its dependency context. Instead of using a one-size-fits-all approach, the system adapts its recommendations to the local characteristics of each API call, improving accuracy for each specific scenario while maintaining overall versatility.

Inventive Principle:

Principle #3Local quality

3Device complexity

If cloud vendors do not extract group dependency relationships between parameters, then API parameter configuration remains simple, but debugging time and troubleshooting difficulty increase

Engineering Contradiction:

ImproveParameter configuration simplicityVSAvoidDebugging time

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

4Reliability

If cloud vendors provide error prompts without panoramic error information, then basic error notification is provided, but troubleshooting completeness deteriorates

Engineering Contradiction:

ImproveError notification accuracyVSAvoidError information completeness

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system merges multiple types of error information into a comprehensive error prompt that includes panoramic error details. By combining basic error notifications with additional contextual information, dependency relationships, and recommended actions, the system provides complete error information while maintaining clear and actionable error messages.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250238356A1Method and Apparatus for Debugging Cloud Service Application Programming Interface API and Related Device

Publication Date: 2025.07.24 HUAWEI CLOUD COMPUTING TECHNOLOGIES CO LTD

AI summary

A method for debugging a cloud service application programming interface (API) on a cloud computing platform includes that the cloud computing platform provides an API configuration interface; then obtains, through the API configuration interface, a first cloud service API selected by the user, obtains a parameter value that is of a first parameter of the first cloud service API and that is entered by the user, and recommends, to the user, a parameter group that is of the first cloud service API and that is related to the first parameter; and obtains a parameter value that is of a second parameter in the parameter group and that is entered by the user. Finally, the cloud computing platform deploys, in a cloud computing resource, a cloud service associated with the first cloud service API, to debug the first cloud service API.