Intelligent automation (IA) is a technique to automate predefined repetitive testing tasks, using various test automation tools and testing scripts. The Future of Intelligent Automation report by Gartner, reported that “Intelligent automation provides huge potential for greater productivity and efficiency in application testing, at a lower cost.”
Test automation is the key to continuous testing and has marked benefits in accuracy, scalability, dependability, enhanced test coverage, time, and effort saving. Intelligent automation contributes to the enormous potential for higher productivity, and efficiency in application testing at a lower cost. The Future of Intelligent Automation report by Gartner, also reported that by 2022, 40% of application development (AD) projects will use AI-enabled test set optimizers that build, maintain, run and optimize test assets."
By working with application leaders to explore IA use cases, such as test optimization, defect prediction, model-based testing, test data generation, and test insights, the team increases application testing agility. Incorporating intelligent automation into application testing services for both new proposals and existing contracts optimizes application testing costs. Applying intelligent automation provides an incredible improvement in quality and an increase in application testing speed. An effective IA testing considers all the changes due to bug fixes or introducing a new feature. Besides modifying and executing the affected test cases and scripts (often referred to as healing) there has to be a service level guarantee that all possible scenarios are covered. The cherry on the top will be if the time and cost are not affected much.
Webomates provides guaranteed regression testing of 24 hours for FULL services, 8 hours of Overnight services, and quick Smoke regression, which gets completed within 15 minutes to a maximum of 1 hour. Our test model ensures that all the relevant test cases are self-healed and retested to reflect any changes in the build release and provide a True Pass and True Failure report.
Model-Based Testing: Generates test cases and test scripts from a model to expand automation coverage.
Test Data Generation: Generates synthetic test data automatically to increase testing QA and reliability and reduce the time needed to prepare test data.
Test Optimization: Recognizing repetitions and similarities in test case & test script records to optimize test case repositories. Cognitive technologies help remove duplicate test cases & scripts, optimize regression test suites, and analytics on test execution results improve test cases & scripts' strength. Machine learning can help optimize execution sequencing. This results in maintaining software QA earlier in the application life cycle, cutting time and effort out of the testing process, & recommend similar test cases to developers.
Defect Prediction: Identifies bugs early and includes machine learning to enhance the accuracy of future-defect classification. Defects are identified and automatically assigned to the correct team member using self-learning algorithms and intelligent classification techniques. Intelligent classification techniques organize specific defect sets into classes. Determine the root cause of defects and incidents, identify gaps in the design and coding areas, understand patterns of the defects that might occur in the future, ramp up or down your team, and cut costs.
Test Insights: AI-based bots generate actionable insights from testing cycles, performance testing analytics, thus comparing results with industry benchmarks to determine program-wide recommendations.
In this blog we talk about the state of intelligent automation in testing where we understand where to implement IA in each application testing phase and what use-cases to examine.