AI Test Script Generation From Support Incidents and Code Fixes

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Solution Overview

Problem

Existing support incident handling and test script generation processes in computer-related tools are manual, time-intensive, and prone to human error, leading to delays and inconsistent outcomes, especially as tools become more complex.

Innovation Solution

Implementing an AI-driven system that automatically extracts information from support tickets, categorizes incidents, generates relevant test cases, and executes tests, reducing manual intervention and improving response time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual support incident handling and test script generation processes are used, then human expertise can be applied to complex issues, but the processes become time-intensive and prone to human error

Engineering Contradiction:

Improveconsistency of test generationVSAvoidtime to generate and execute tests

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables automated self-service through AI-driven test script generation that processes support incidents without manual intervention. The automated system extracts information from incident reports, generates relevant test scripts, and executes them automatically, eliminating the need for manual test creation while maintaining consistency and reducing errors.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes of test script creation and execution are replaced with an automated AI-based system. The mechanical typing and manual analysis performed by humans are substituted with automated information extraction, natural language processing, and script generation algorithms that operate continuously without fatigue or error.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual test script generation is used, then tests can be customized for specific incidents, but the process becomes time-intensive and delays response

Engineering Contradiction:

Improvecustomization of tests to incidentsVSAvoidresponse speed to incidents

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system performs preliminary actions by pre-processing support incident reports as they arrive, automatically extracting key information and generating test scripts in advance. This preliminary automated processing ensures that customized tests are ready for execution immediately when needed, eliminating delays associated with manual test creation while maintaining incident-specific customization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically changes parameters based on incident characteristics by adjusting test script generation parameters according to the specific incident type, severity, and affected components. This allows the automated system to customize tests for each incident while maintaining high-speed automated processing, achieving both adaptability and productivity.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If more comprehensive testing is implemented, then code quality improves, but the complexity of the testing system increases

Engineering Contradiction:

Improvecode qualityVSAvoidtesting system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The AI-driven testing system performs multiple functions within a single unified platform: it processes support incidents, extracts relevant information, generates customized test scripts, schedules executions, and analyzes results. This multi-functional approach achieves comprehensive testing for improved code quality while avoiding the complexity of multiple separate manual processes through centralized automated management.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250348414A1Systems and/or methods for automatically deriving and executing computer test scripts

Publication Date: 2025.11.13 SOFTWARE AG

AI summary

Certain example embodiments herein relate to techniques that help automatically derive test scripts based on incidents reported using a trouble ticket reporting system or the like. Information is extracted from a support incident report related to an underlying incident reported via a ticket system. The extracted information includes one or more elements of a computer-implemented product implicated by the underlying incident and a category of incident. A test case is generated based thereon. If there is no test matching the generated test case already existing in a test repository, a new test based on the test case is generated using a generative artificial intelligence (AI) model, and the generated new test is stored to the test repository. Responsive to a developer fix being committed, one or more tests relevant to the developer fix is/are automatically identified and automatically executed.