Autonomous API Code Usage Summarization via ML Clustering
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Solution Overview
Problem
Existing technologies face challenges in efficiently summarizing code usage associated with web application programming interface (API) requests, as code examples and usage information are often dispersed and difficult to locate within formal documentation or buried in data repositories, making it hard for humans to analyze and maintain.
Innovation Solution
A computer-implemented method and system that employs machine learning processes to evaluate data from a repository, generating usage summaries by aggregating statistics and clustering similar API requests, thereby identifying representative requests and filtering for quality standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If code usage information is stored in formal documentation or data repositories, then the information is preserved and accessible, but it becomes dispersed and difficult to locate
Solution Approach 1:
The patent merges scattered code usage information from multiple sources (formal documentation, data repositories, code examples) into a unified structured format with standardized fields such as API endpoint, parameters, request body, and response. This consolidation makes the information easily locatable while preserving all original sources.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between dispersed code usage information and users. This system automatically extracts, standardizes, and organizes code examples from various sources into a centralized repository with consistent structure, enabling easy retrieval without users needing to search multiple original sources.
2Reliability
If human analysts manually review and summarize code usage, then quality control is maintained, but the process is time-consuming and difficult to scale
Solution Approach 1:
The patent implements a self-service system where code usage information automatically extracts and summarizes itself from various sources without requiring manual human intervention. The system uses automated parsing, pattern recognition, and template-based generation to maintain quality standards while processing vast amounts of code examples at scale.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computational system that uses structured extraction rules, validation algorithms, and standardized templates. This substitution maintains reliability through consistent application of extraction criteria while dramatically increasing productivity by processing code examples automatically without human time constraints.
3Loss of information
If comprehensive code usage data is collected from all sources, then complete coverage is achieved, but the data becomes overwhelming and difficult to analyze
Solution Approach 1:
The patent segments comprehensive code usage data into distinct standardized components including API endpoint, parameters, request body, response, and metadata. Each code example is divided into structured fields with specific schemas, transforming overwhelming unstructured data into manageable organized segments that are easier to analyze and process.
Solution Approach 2:
The patent applies parameter changes by transforming raw code usage data into standardized parameters with consistent data types, formats, and validation rules. Code examples are converted from heterogeneous formats into uniform structured parameters, reducing analysis complexity while maintaining complete information coverage through systematic parameter transformation.
Data Source
AI summary
Techniques for autonomously generating a code usage summary associated with a web application programming interface request are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating is based on a defined machine learning process. Also, the computer-implemented method can comprise generating, by the system, a usage summary of the data, wherein the usage summary is based on a statistic derived from a web application programming interface request, and the web application programming interface request is associated with the data.