Mobile Log Heatmap Auto Test Case Generation

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Solution Overview

Problem

Conventional methods for generating test cases and testing data are resource-intensive and inefficient, particularly in application development, as they require manual processes that cannot achieve 100% or near 100% test case coverage.

Innovation Solution

A system for mobile log heatmap-based auto test case generation that continuously logs user actions and data flows in a production environment, using machine learning to identify navigation paths, generate a navigation network graph, aggregate testing data, and sanitize sensitive information to create test cases and data for a lower-level testing environment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual processes are used to generate test cases and testing data, then resource consumption is high and efficiency is low, but test case coverage cannot achieve 100% or near 100%

Engineering Contradiction:

Improvetest case generation efficiencyVSAvoidresource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system automatically generates test cases and testing data by leveraging user activity data from production environments. The machine learning mechanism autonomously identifies navigation paths and generates test cases without human intervention, making the testing process self-service and eliminating the need for resource-intensive manual test case creation while achieving comprehensive coverage

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes with an automated machine learning system. The machine learning mechanism analyzes user behavior patterns and automatically generates test cases, substituting the manual mechanical approach of testers creating test cases with an intelligent automated system that achieves superior efficiency and coverage

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual processes are used to generate test cases, then resource consumption increases, but test case coverage remains insufficient

Engineering Contradiction:

Improvetest case coverageVSAvoidtesting system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system introduces a machine learning mechanism as an intermediary between production user activity data and test case generation. This intermediary automatically processes user navigation patterns and transforms them into comprehensive test cases, achieving 100% or near 100% coverage without requiring complex manual testing systems

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system creates copies of real user navigation paths from production environments and uses these copies as the basis for generating test cases. By replicating actual user behavior patterns, the system achieves comprehensive coverage while maintaining a relatively simple testing infrastructure

Inventive Principle:

Principle #26Copying

3Measurement precision

If testing data from production environment is used, then test case accuracy improves, but sensitive information exposure risk increases

Engineering Contradiction:

Improvetest case accuracyVSAvoidsensitive information exposure

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system extracts only the necessary navigation path information and behavioral patterns from production testing data while leaving behind sensitive information. The machine learning mechanism processes and filters data to extract purely structural and behavioral characteristics needed for accurate test case generation without exposing sensitive user or system information

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system applies different quality characteristics to different parts of the testing data. Sensitive fields are removed or anonymized while preserving the structural and behavioral patterns needed for test case accuracy. This local differentiation allows the system to maintain high test case accuracy while eliminating sensitive information exposure risks

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11593255B2Mobile log heatmap-based auto testcase generation

Publication Date: 2023.02.28 BANK OF AMERICA CORP

AI summary

A system is provided for mobile log heatmap-based auto test case generation. In particular, the system may continuously track and log user actions and data flows for applications within the production environment. Based on the logs, the system may generate a navigation network graph through which the system may identify all possible navigation paths that may be taken by the user to access certain functions or screens of the application. Once the paths have been identified, the system may collect and sanitize testing data based on user session and system interaction data in the production environment. The testing data may then be used to drive the development of the next release or version of the application.