Machine Learning URL Error Correction in REST APIs
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Solution Overview
Problem
Existing web applications face issues with URL and URI request errors, particularly due to incorrect encoding of special characters, which can lead to failed REST API calls and frustration for users, as these errors may not be detected during development but manifest in production environments.
Innovation Solution
A computer-implemented method and system that uses machine learning to detect and correct URL and URI path errors by identifying patterns from historical requests, replacing erroneous reserved characters like extraneous slashes with their corresponding codes, ensuring proper encoding and matching valid URL patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional percent encoding is used to handle special characters in URLs, then the URL structure remains simple and readable, but encoding errors occur when reserved characters are incorrectly replaced, leading to failed REST API calls
Solution Approach 1:
The system implements feedback by analyzing historical successful REST API requests to learn correct URL encoding patterns. When a new request is received, the system compares it against learned patterns, detects encoding errors, and corrects them by replacing erroneous reserved characters with their proper percent-encoded equivalents based on historical success data.
Solution Approach 2:
The system performs preliminary action by pre-processing incoming REST API requests to detect and correct URL encoding errors before the requests are executed. This proactive correction prevents API call failures by ensuring proper encoding of special characters in advance, rather than waiting for failure and then retrying.
2Measurement precision
If URL encoding rules are strictly enforced during development testing, then encoding errors can be detected early, but false positives occur in production environments where legitimate URL structures differ from development patterns
Solution Approach 1:
The system applies dynamics by making the URL validation rules adaptive rather than static. Instead of enforcing fixed encoding rules, the system dynamically learns acceptable URL patterns from historical successful requests in the specific production environment, allowing it to adapt to legitimate variations in URL structures while still detecting actual encoding errors.
Solution Approach 2:
The system implements self-service by automatically learning and updating its own validation rules from historical data without requiring manual configuration or intervention. The machine learning component continuously improves its understanding of correct URL patterns for each specific API endpoint, enabling the system to self-adjust to environment-specific conventions.
3Reliability
If machine learning is used to learn URL patterns from historical requests, then encoding errors can be corrected with high accuracy, but the system requires substantial historical data and processing time to establish reliable patterns
Solution Approach 1:
The system applies partial action by focusing the machine learning process on specific, high-value URL patterns and encoding scenarios rather than attempting to learn all possible URL structures. This targeted approach allows the system to achieve high accuracy for the most common and critical encoding errors with a reasonable amount of historical data, rather than requiring exhaustive coverage of all potential URL variations.
Data Source
AI summary
Technology for making corrections to URL and/or URI requests (herein generically referred to as “UR requests”) based on machine learning of patterns that can be gleaned from previously received UR requests. The patterns are then used to correct new UR requests that are received with errors in the resource location path. In some embodiments, the errors detected and corrected are path errors. In some embodiments the path error is an erroneous inclusion of an extraneous reserved slash (“/”) character, which, when detected, is replaced in the UR string with its corresponding proper code (for example, - -%2F- -).