Automatic Test Script Generation from Incident Reports

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Solution Overview

Problem

Complex software systems face increased errors and longer debugging times due to their complexity, especially when used across different computing environments, making it difficult to reproduce and resolve software errors and test solutions effectively.

Innovation Solution

Automatically generating test scripts from incident reports using natural language processing and machine learning to reproduce errors and determine if they have been resolved, allowing for automated execution and user interface presentation of test results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If software complexity increases to support large enterprise applications, then functionality and versatility improve, but error rate and debugging difficulty increase

Engineering Contradiction:

ImprovefunctionalityVSAvoiderror rate

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system automatically generates test scripts from incident reports without requiring manual intervention. The natural language processing and machine learning components enable the system to self-serve by autonomously creating, executing, and analyzing test cases to verify bug fixes.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual testing and debugging processes are replaced with automated machine learning-based test script generation and execution. The mechanical human effort in reproducing bugs and verifying fixes is substituted with computational algorithms that process incident reports and execute tests automatically.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If software complexity increases across different computing environments, then adaptability improves, but time and effort to locate and test errors increase

Engineering Contradiction:

Improveenvironment compatibilityVSAvoiddebugging time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The test script generation system is designed to be universal across different computing environments. The machine learning model processes incident reports and generates environment-specific test scripts automatically, enabling the same system to handle multiple platforms, hardware configurations, and software versions without requiring separate manual testing procedures for each environment.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system performs preliminary actions by automatically generating test scripts as soon as an incident report is received, before the bug fix is even implemented. This allows test scripts to be ready and waiting for execution once the fix is deployed, eliminating the need to wait and create tests after debugging begins.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If manual test script generation is used to verify bug fixes, then testing accuracy improves, but productivity and speed of error resolution decrease

Engineering Contradiction:

Improvetesting accuracyVSAvoiderror resolution speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

Manual test script generation is replaced with automated machine learning-based generation. The system substitutes human testers with algorithms that can process incident reports, generate appropriate test cases, and execute them automatically, maintaining testing rigor while dramatically increasing speed and throughput.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning model acts as an intermediary between incident reports and test execution. It translates unstructured incident descriptions into structured, executable test scripts, serving as a bridge that maintains accuracy while enabling automation. This intermediary layer ensures that the generated tests faithfully represent the original bug conditions.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If automated test script generation is implemented, then productivity and speed improve, but system complexity and development effort increase

Engineering Contradiction:

Improvetest generation speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system is designed to be self-sufficient, automatically generating test scripts from incident reports without requiring complex manual configuration or extensive development effort. The machine learning components handle the complexity internally, allowing users to benefit from automated testing without needing to understand or manage the underlying system complexity.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11263118B2Automatic test scenario generation

Publication Date: 2022.03.01 SAP SE

AI summary

Techniques and solutions are described for facilitating software testing. A test script is automatically generated from an incident report. Natural language processing can be used, at least in part, to generate the test script. The test script can be manually or automatically executed, including automatically executing the test script upon the occurrence of trigger event or according to a schedule. A trigger event can include determining that a software update has been deployed and is available for testing. It can be determined whether test execution indicates that a particular program behavior, such as an error, has been resolved. Machine learning can be used to classify program behavior. A user interface screen can be rendered to present test results.