TEST AUTOMATION
1Gartner,Market Guide for AI-Augmented Software-Testing Tools (Nov 28 2022). GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved
A paradigm change in test data management
Test data management is a critical aspect of test planning. Software testers cannot test everything or eliminate all risk, thus maximizing test coverage efficiently is critical for software quality and time delivery. However, organizations are often restricted to use their production data for testing and analytics, mostly due to the presence of personal sensitive data. This especially concerns privacy regulations such as the General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA).
This barrier limits the set of validations that can be run in the testing environment and organizations often resort to testing in production. This kind of poor test data management practices can slow down development and testing efforts, and erode the confidence of software engineers in the quality of the products.
AI can increase the efficacy and automation of test tools, enabling test case generation, and replacing production data with synthetic test data to address privacy issues. Synthetic test data gives the user the capacity to amplify test coverage and define data model relationships. This in turn gives software engineers full flexibility to scale and subset test data in short generation cycles. Learn more about synthetic data.
Limitations of production data for testing
production test data
- Stringent access requirements
- Risk of privacy compliance violation
- Missing desired characteristics and test cases
- May contain biases
- Data complexity makes subsetting problematic
synthetic test data
- Increased data availability
- Privacy-safe data
- Expanded attributes and corner cases
- Balanced data
- Full control over scale
The value of automated test integration
The shift to DevOps in order to deliver high-quality software at an increasingly faster pace is driving automation. DevOps requires improved support for test automation including change-based test execution and risk assessment. This approach is triggered via Continuous Integration (CI) tools increasingly powered and enhanced by AI technologies.
Skymantics approaches test automation as a critical pillar of digital transformation. According to Gartner, 51% of industry entities prioritize software quality as a performance objective. Especially as software systems shift to microservice architectures, modular design of testing components becomes the norm. As such, we accompany our customers through the integration process into automated modular test workflows to ensure they become an indispensable block in its software delivery strategy.
Do you have software testing needs you would like to discuss? Contact us to query about our automated testing capabilities and request a demo today.