Intelligent Code Update for Test Automation Engine

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

In a DevOps and continuous delivery model, traditional testing methods are inadequate as they do not account for the need for regular updates of automated test scripts to ensure software quality and efficiency, leading to potential errors and inefficiencies in software testing.

Innovation Solution

A system for intelligent code update in a test automation engine that utilizes machine learning algorithms to classify exceptions, generate exception handling routines, and update automated test scripts, incorporating a knowledge management database and dashboard reporting for continuous improvement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional testing methods are used at the end of development cycle, then testing completeness is improved, but software delivery speed deteriorates

Engineering Contradiction:

Improvetesting completenessVSAvoidsoftware delivery speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary actions by continuously executing automated test scripts throughout the development cycle rather than waiting until the end. Test scripts are automatically updated and re-executed in real-time, allowing testing to occur concurrently with development activities, thus maintaining testing completeness while accelerating delivery speed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by automatically analyzing test results and generating updates to test scripts based on detected anomalies. This closed-loop feedback enables continuous improvement of test coverage without manual intervention, ensuring comprehensive testing while maintaining rapid iterative development cycles.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If automated test scripts are frequently updated manually, then testing accuracy is improved, but time consumption increases

Engineering Contradiction:

Improvetesting accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables self-service by automatically generating updates to test scripts based on anomaly detection and machine learning analysis. The system autonomously identifies required modifications, generates updated script code, and implements changes without human intervention, thereby maintaining high testing accuracy while eliminating the time cost of manual script updates.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces the mechanical process of manual script updating with an automated computational system. Machine learning algorithms analyze test failures and automatically generate corrective code modifications, substituting human manual labor with intelligent automation that achieves both high accuracy and rapid execution.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If machine learning algorithms are implemented for exception classification, then exception handling accuracy is improved, but system complexity increases

Engineering Contradiction:

Improveexception handling accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces an intermediary layer consisting of pre-trained machine learning models that serve as mediators between raw exception data and handling decisions. These models are trained offline on historical data and then deployed as ready-to-use classifiers, adding accuracy without requiring complex real-time processing infrastructure during test execution.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The machine learning models are trained in advance on historical exception data before deployment. This preliminary training action prepares the models to accurately classify exceptions during runtime without requiring complex training procedures during actual testing, thus achieving high accuracy while keeping the runtime system relatively simple.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11263117B2System for intelligent code update for a test automation engine

Publication Date: 2022.03.01 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for intelligent code update for a test automation engine. The present invention is configured to receive, from a test automation engine, one or more exceptions associated with one or more automated test scripts; initiate an execution one or more machine learning algorithms on the one or more exceptions; classify the one or more exceptions into one or more exception types; generate an exception handling routine based on at least classifying the one or more exceptions into the one or more exception types; initiate an execution of the exception handling routine on the automated test script, wherein the exception handling routine comprises computer instructions for updating a source code of the one or more automated test scripts; and initiate an execution of the one or more updated automated test scripts on the test automation engine.