Intelligent Code Interceptor for No-Code Test Generation

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Solution Overview

Problem

Existing application testing methods are time-consuming, costly, and error-prone, often requiring significant effort and resources, and do not effectively utilize real data without additional scripting efforts.

Innovation Solution

A computing platform using generative artificial intelligence to automatically generate and execute test input information, compare results, and deploy software applications without additional coding, leveraging an intelligent code interceptor to capture and summarize test data for automated testing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual test cases are written to ensure code coverage, then testing reliability is improved, but time consumption and cost increase

Engineering Contradiction:

Improvetesting reliabilityVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by automatically generating test cases from existing code and requirements before manual testing begins. The automated test case generation creates initial test suites that cover code paths, reducing the time needed for manual test case writing while maintaining reliability through systematic code coverage analysis.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of test cases by generating multiple test scenarios from a single code unit or requirement. Instead of manually writing each test case, the system automatically generates numerous test cases that replicate different execution paths, input conditions, and edge cases, thereby improving reliability without proportionally increasing time consumption.

Inventive Principle:

Principle #26Copying

2Manufacturing precision

If statistical testing of code units is performed, then code coverage is improved, but resource consumption increases

Engineering Contradiction:

Improvecode coverageVSAvoidprocessing power

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The system applies local quality by focusing testing resources on specific code units that require coverage rather than uniformly testing all code. It identifies critical code paths and prioritizes test case generation for those areas, improving code coverage precision while reducing overall processing power consumption by avoiding redundant testing of already-covered sections.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system changes parameters by dynamically adjusting test case generation based on code complexity, coverage requirements, and resource availability. It modifies testing depth, breadth, and intensity according to the specific code unit being tested, thereby achieving effective code coverage while optimizing processing power usage rather than applying a fixed high-resource testing approach to all code.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If test cases are created without real data, then resource requirements are reduced, but testing accuracy deteriorates

Engineering Contradiction:

Improvetesting efficiencyVSAvoidtesting accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system uses an intermediary approach by introducing synthetic test data generation as a mediator between resource constraints and testing accuracy. Instead of directly using real production data (which would require additional resources) or simple placeholder data (which reduces accuracy), the system generates realistic synthetic test data that mimics real data characteristics, thereby maintaining testing accuracy while preserving productivity.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If additional scripting efforts are made to use real data, then testing realism is improved, but device complexity increases

Engineering Contradiction:

Improvetesting realismVSAvoidscripting complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system applies self-service by enabling automated test case generation that serves its own data needs without requiring external scripting intervention. The test case generation mechanism automatically creates and manages test data, eliminating the need for separate scripting efforts to prepare real data, thereby reducing device complexity while maintaining testing realism through automated data synthesis.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250298720A1Intelligent code interceptor for generating a no-code automation test suite using generative artificial intelligence (AI)

Publication Date: 2025.09.25 BANK OF AMERICA CORP

AI summary

A computing platform may receive test input information comprising input information to validate code for an application. The computing platform may test the code by inputting the test input information into the application and executing the code to produce initial test results. The computing platform may store the test input information/initial test results in a table. The computing platform may identify that subsequent testing should be initiated. The computing platform may automatically identify the test input information and the corresponding initial test results using the stored table. The computing platform may automatically test the application by inputting the test input information into the application and executing the application to produce automated test results. The computing platform may compare the automated test results to the initial test results. Based on identifying that the automated test results match the initial test results, the computing platform may deploy the application.