Production Navigational Graph for Software Test Coverage

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Solution Overview

Problem

Enterprise organizations face challenges in identifying and correcting performance issues in software applications due to the difficulty in testing all navigational paths and links in production environments, leading to inefficiencies in maintaining a stable computing infrastructure.

Innovation Solution

A computing platform that generates production and testing navigational graphs to highlight gaps between user navigation and testing activity, using machine learning to prioritize error correction strategies based on traffic patterns and error frequencies, and providing an interactive interface for developers to address uncovered paths and errors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If comprehensive testing of all navigational paths and links is performed in the testing environment, then test coverage is improved, but the time and resources required for testing increase significantly

Engineering Contradiction:

Improvetest coverageVSAvoidtesting time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis by comparing production navigation logs with testing activity logs to generate a coverage graph before full testing is executed. This identifies gaps in test coverage in advance, allowing testers to focus only on uncovered paths and links, thereby reducing the time required for comprehensive testing while maintaining high test coverage.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a feedback loop where production environment navigation data is continuously collected and compared against testing activity. The coverage graph provides feedback on which paths have been tested and which remain uncovered, enabling iterative improvement of test coverage over time without requiring exhaustive testing in each release cycle.

Inventive Principle:

Principle #23Feedback

2Productivity

If rapid deployment and upgrades are performed to improve productivity, then the speed of delivering new features is improved, but the risk of introducing errors and performance issues increases

Engineering Contradiction:

Improvedeployment speedVSAvoidsoftware stability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

Before deployment, the system generates a coverage graph that identifies untested paths and links that may contain errors. This preliminary identification allows teams to address potential issues before they reach production, enabling faster deployment with reduced risk of introducing errors.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides continuous feedback by comparing production navigation patterns with testing coverage. This feedback mechanism allows rapid identification of newly introduced errors after deployment, enabling quick correction while maintaining high deployment velocity.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If extensive testing activity is performed to improve error detection, then the accuracy of identifying performance issues is improved, but the network resources and bandwidth utilization increase

Engineering Contradiction:

Improveerror detection accuracyVSAvoidnetwork resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system extracts and analyzes only the critical data elements needed for coverage analysis - specifically navigation logs and testing activity logs - rather than performing exhaustive testing of all system functions. This selective extraction approach maintains high error detection accuracy while minimizing network resource consumption by focusing only on navigational path coverage.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS11232019B1Machine learning based test coverage in a production environment

Publication Date: 2022.01.25 BANK OF AMERICA CORP

AI summary

Aspects of the disclosure relate to machine learning based test coverage in a production environment. A computing platform may retrieve, for a software application, first log data associated with a production environment, and generate a production navigational graph where a node represents a webpage visited by a user, and an edge represents a navigational link between two webpages. Likewise, second log data associated with a testing environment may be retrieved, and a testing navigational graph may be generated, where a node represents a webpage tested by a developer, and an edge represents a navigational link between two webpages. Then, the computing platform may generate a coverage graph indicative of a gap between the navigation activity and the testing activity. Subsequently, the computing platform may provide, via an interactive graphical user interface, the coverage graph, where nodes and edges of the coverage graph are associated with selectable visual objects.