and the next wave of test optimization
Artificial intelligence (AI) and machine learning can train software to understand test input data – very similar to manual testing activities. AI can optimize every aspect of testing – test case optimization, migration, environment management, automation execution, result analysis and defect prediction.
More and more enterprises are embracing Agile and DevOps to accelerate their digital efforts, but test/QA organizations may not be equipped to adapt to the impact these initiatives have on their workload — due to a lack of expertise, limited knowledge of automation tools, and an overwhelming test case backlog.
AI-based testing can provide benefits in many aspects of the test/QA process.
The readiness of Test/QA organizations for AI-based testing varies widely. The chart on the left provides a general benchmark for organizations to measure themselves against.
Need: Remove ‘dead’ test cases from a repository of over 150,000 to streamline automation efforts.
Need: Optimize testing efforts within risk parameters using past defect trends and future quality predictions.
Need: Streamline automation process for 25,000+ test cases on 4 mobile devices and 2 browser configurations weekly.
A new AI-powered quality engineering suite comprised of intelligent testing services, best practices, and pre-configured BOTs.
Find out how. Visit here to get a free assessment of your organization’s readiness to implement AI for test optimization.