AI-Driven Dynamic Test Data Generation for CI/CD Pipelines
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Solution Overview
Problem
Software testing in continuous integration and delivery environments is inefficient due to the time-consuming and costly process of generating test datasets, which often miss errors and require manual manipulation, leading to unreliable results.
Innovation Solution
A computing platform uses AI and machine learning models to generate dynamic test datasets based on historical data and test code deployment parameters, automating the process of creating and modifying test data sets to ensure accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual manipulation steps are used to generate test datasets, then the test results may be more accurate for the specific software being tested, but the process becomes time intensive and costly
Solution Approach 1:
The system enables self-service automated generation of test datasets by leveraging historical data and AI models. The computing platform automatically creates, configures, and modifies test datasets without requiring manual manipulation steps, while still ensuring accuracy through intelligent algorithms that understand the specific software being tested.
Solution Approach 2:
The system performs preliminary action by pre-processing historical data and pre-configuring test datasets before actual testing begins. The AI model learns from historical test data in advance, enabling rapid generation of accurate test datasets when needed without time-consuming manual preparation during the testing phase.
2Reliability
If manual manipulation steps are used to generate test datasets, then the test results may be more accurate for the specific software being tested, but the process becomes costly
Solution Approach 1:
The system enables self-service automated generation of test datasets by leveraging historical data and AI models. The computing platform automatically creates, configures, and modifies test datasets without requiring manual manipulation steps, while still ensuring accuracy through intelligent algorithms that understand the specific software being tested.
Solution Approach 2:
The system uses copying by leveraging historical test data as templates and patterns for generating new test datasets. Instead of manually creating datasets from scratch, the AI model copies and adapts proven test scenarios from historical data, reducing both cost and time while maintaining accuracy.
3Productivity
If automated test data generation is implemented, then efficiency and scalability are improved, but the test datasets may miss errors that manual manipulation would detect
Solution Approach 1:
The system implements feedback by continuously learning from test execution results and historical data. The AI model analyzes test outcomes, identifies patterns, and uses this feedback to improve future test dataset generation, ensuring that automated processes become increasingly accurate at detecting errors while maintaining high efficiency.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting test dataset parameters based on the specific software being tested and historical performance data. The AI model modifies test parameters, data values, and test scenarios to match the characteristics of the target software, enabling automated generation to achieve both efficiency and reliable error detection.
4Adaptability or versatility
If AI models are used to generate test datasets dynamically, then the testing process becomes more scalable and adaptable, but the system complexity increases
Solution Approach 1:
The system applies universality by designing a multi-functional computing platform that handles multiple testing scenarios, data types, and software applications through a single AI-driven framework. The platform can generate test datasets for various purposes (functional testing, performance testing, security testing) using the same core infrastructure, reducing overall system complexity despite high adaptability.
Data Source
AI summary
Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.