AI-Driven Test Data Generation for CI/CD Environments
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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 models to dynamically generate test datasets based on historical data and test code deployment parameters, automating the process of creating and modifying test data sets using AI engines for error correction and anomaly checks, thereby improving the efficiency and accuracy of software testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual manipulation steps are used to generate test datasets, then the datasets can be prepared with specific control, but the process becomes time intensive and costly
Solution Approach 1:
The system enables self-service test data generation where the AI model automatically creates test datasets without requiring manual intervention. The model learns from historical data and deployment parameters to autonomously generate appropriate test data, eliminating the time-consuming manual preparation process while maintaining data quality and relevance for testing purposes
Solution Approach 2:
The patent replaces the mechanical manual data preparation process with an AI-based automated system. The AI model processes historical data and deployment parameters to generate test datasets automatically, substituting human manual operations with intelligent automation that is both faster and scalable
2Manufacturing precision
If manual manipulation steps are used to generate test datasets, then specific control over data can be maintained, but errors in the tested software may be missed
Solution Approach 1:
The system incorporates feedback mechanisms where test results and execution outcomes are fed back into the AI model to continuously improve data generation quality. This feedback loop ensures that the model learns from previous testing experiences and generates more reliable test datasets that can detect software errors more effectively
Solution Approach 2:
The automated AI system replaces manual data preparation with intelligent algorithms that can systematically explore edge cases and potential error scenarios. The model's ability to process historical data and generate diverse test cases improves the reliability of software testing by reducing the likelihood of missing errors
3Productivity
If AI models are used to generate test datasets, then efficiency and automation are improved, but the system complexity increases
Solution Approach 1:
The AI model serves multiple functions within the testing system: it generates test datasets, validates data quality, adapts to different deployment environments, and learns from test results. This multi-functionality consolidates what would otherwise require multiple separate tools and processes into a single unified system, managing complexity while improving productivity
Solution Approach 2:
The system manages complexity by dynamically adjusting AI model parameters and configuration based on the specific testing requirements and historical data available. The model adapts its behavior and generation strategies based on deployment parameters and test results, allowing efficient operation without requiring overly complex fixed architectures
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.