Dynamic API Maturity Scoring for Changing User Behavior
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Solution Overview
Problem
Existing API maturity models are static and do not account for dynamic changes in API performance and user feedback, leading to inaccurate assessments of API quality and resource inefficiencies in digital commerce systems.
Innovation Solution
A dynamic API maturity model that incorporates producer-based, context-based, and behavior-based layers to calculate a maturity score, dynamically adjusting based on customer feedback and usage metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static API maturity models are used to assess API quality, then the assessment process is simple and quick, but the accuracy of API quality assessment is low and cannot reflect dynamic changes
Solution Approach 1:
The patent transforms static API maturity models into dynamic models by continuously collecting user interaction data and feedback, then updating API maturity scores in real-time. The system monitors usage patterns, error rates, and user feedback to dynamically adjust maturity assessments, enabling the model to reflect actual API performance changes rather than relying on fixed predetermined criteria.
Solution Approach 2:
The patent implements feedback mechanisms by collecting user interaction data and API usage metrics, then using this feedback to continuously refine and update API maturity assessments. The system analyzes user feedback, error reports, and usage patterns to adjust maturity scores, creating a closed-loop assessment process that improves accuracy over time while adapting to changing API performance.
2Productivity
If existing API maturity models are used, then implementation is straightforward, but resource inefficiencies occur including computer processing delays, data storage shortages, and communication network congestion
Solution Approach 1:
The patent enables the API system to self-monitor and self-optimize by automatically collecting usage data, analyzing performance metrics, and adjusting operations based on real-time feedback. The system self-regulates resource allocation based on actual demand patterns, eliminating the need for manual intervention and optimizing resource utilization to reduce energy loss and improve productivity.
Solution Approach 2:
The patent dynamically adjusts system parameters based on real-time API performance data and user feedback. The system changes operational parameters such as response thresholds, data collection frequencies, and resource allocation based on actual usage patterns, enabling adaptive optimization that improves transaction efficiency while reducing resource consumption compared to static models.
Data Source
AI summary
Techniques for application programming interface management in information processing systems are disclosed. For example, a method obtains data associated with one or more users indicative of interactions of the one or more users with an application programming interface configured to enable the one or more users to interact with an information processing system. The method computes a score for the application programming interface based on at least a portion of the obtained data, the computed score is indicative of a maturity level of the application programming interface. In some further examples, the data may correspond to factors in one or more categories (e.g., model layers) of user interaction with the application programming interface such that, as data corresponding to one or more of the factors changes, the score indicative of the maturity level of the application programming interface changes. The score may be used to modify the application programming interface.