Script Error Quantification for Online Retail Platforms
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Solution Overview
Problem
Script errors on online retail platforms go unnoticed by developers in real-time, leading to potential revenue loss and user frustration due to the tedious process of testing and debugging across various computing platforms, and the lack of immediate reporting of errors to website owners.
Innovation Solution
A method and system for quantifying the impact of script error exceptions on online retail platforms by selecting performance metrics, retrieving normal and abnormal values, comparing them to determine a performance impact score, and prioritizing errors based on their business impact, using a tracking tag and analytic system to aggregate and analyze script activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers manually test and debug every script on each computing platform, then script errors can be detected, but the process becomes tedious and impractical
Solution Approach 1:
The system enables self-service error detection by automatically monitoring script execution on user devices and reporting errors back to developers. The analytic system collects script activity data from multiple users across different platforms and identifies errors without requiring manual developer intervention for each test case.
Solution Approach 2:
The system implements continuous feedback loops where script execution data is collected from production environments, analyzed for errors, and reported back to developers in real-time. This feedback mechanism allows errors to be detected automatically as they occur in the wild, eliminating the need for exhaustive manual testing.
2Device complexity
If script errors are not reported in real-time, then system complexity is reduced, but revenue loss and user frustration increase
Solution Approach 1:
The patent introduces an intermediary analytic system that sits between the script execution environment and the developers. This intermediary automatically collects, analyzes, and reports script errors, serving as a mediator that translates raw script activity data into actionable error reports without requiring complex direct integration between all system components.
Solution Approach 2:
The system performs preliminary actions by proactively monitoring and detecting script errors before they significantly impact revenue or user experience. By continuously analyzing script activity data in advance, the system can identify and report errors early, allowing developers to fix issues before they cause substantial business harm.
3Measurement precision
If comprehensive script testing is performed across all platforms, then error detection capability improves, but the ease of operation deteriorates
Solution Approach 1:
The system automates the entire error detection workflow, allowing it to serve itself without requiring developers to manually configure testing across multiple platforms. The analytic system independently collects data, identifies errors, and generates reports, freeing developers from the burden of comprehensive manual testing while maintaining high detection precision.
4Reliability
If real-time error monitoring is implemented, then script error identification improves, but system complexity increases
Solution Approach 1:
The patent employs an intermediary analytic system that simplifies real-time monitoring by acting as a centralized coordination point. Rather than requiring complex direct communication between all user devices and all developer systems, the intermediary collects data from multiple sources, processes it centrally, and distributes relevant information, thereby reducing overall system complexity while enabling real-time error identification.
Data Source
AI summary
A system and method for quantifying impact of script error exceptions on performance of an online retail platform. A method includes selecting at least one performance metric for a webpage, wherein the selected performance metric has an impact due to at least one script error exception encountered on the webpage visited by a first user device; retrieving a normal value for each of the at least one selected performance metric for the webpage visited by a second user device; retrieving an abnormal value for each of the at least one selected performance metric for the webpage visited by the second user device; comparing the abnormal value to the normal value of a respective selected performance metric; and determining a performance impact score based on the comparison, wherein the performance impact score is indicative of a reduction in a performance metric of each of the least one selected performance metric.