Black Box Testing, also known as functional testing or behavioral testing, requires the testers to evaluate the functionality and usability of the software without delving deeper into the details of the code. Black box testing verifies and validates the functionality of the software from an end-user perspective, and also checks for any interface issues.
Black box testing has many advantages that work in favor of the testing teams, however, it comes with certain baggage too. The following table lists the pros and cons of black-box testing and the entailing section elaborates on how AI test automation enhances its value by working on its drawbacks and improvising it further to provide a holistic testing setup.
AI test automation — The facilitator for enhancing the value of Black box testing
In an increasingly agile environment and continuous testing, the drawbacks of Black-box testing may impede overall feature velocity. Test automation addresses this issue to a certain extent and has marked benefits in terms of accuracy, scalability, dependability, enhanced test coverage, time, and effort saving. AI automation further adds the dimension of machine learning capabilities to test automation by emulating human behavior of understanding, learning, and implementing, without any actual human involvement. It can spot anomalies, learn from patterns, analyze the data, and then if required, can upgrade the test scripts (self-healing).
Improved test coverage, accuracy, and reliability AI test automation applies machine learning to identify various logical paths and test conditions to generate the test cases. AI-assisted testing tools can also identify error-prone areas and lay extra emphasis on designing test cases for the same. This aids in having comprehensive test coverage. AI test automation helps in validating the test cases for a variety of test inputs for all the identified test conditions in the module under test.
AI testing tools constantly learn and update their knowledge base with every test cycle based on test result analysis. They apply that knowledge to improve software testing by detecting even minor changes and update the test scenarios accordingly. Webomates CQ applies AI and ML algorithms to its self-healing test automation framework to dynamically understand the changes made to the application and modifies the testing scope accordingly.
Our AI codeless engine effortlessly modifies (heal) the test cases, scripts and re-executes them within the same test cycle. Read more about self-healing in our blog “Self-healing — Automate the automation”.
Efficient defect management AI test automation has efficient defect management with effective defect tracking capabilities and detailed reporting with analysis.
Improved defect traceability and defect prediction contribute to optimizing the test strategies. Also, false positives in testing slow down the development speed.
Webomates’ ingenious AI defect predictor can identify false positives with 99% accuracy. It not only differentiates true failures from false failures but also helps in creating a defect using the AI engine for True Failures.
Our AI engine also predicts possible defects based on the test result analysis and facilitates the generation of new test cases, thus ensuring a good test coverage.
Read more about defect management and intelligent analytics in our blogs “Defect triaging” and “Intelligent analytics with AI”. It will give you detailed insight and a deeper understanding of how Webomates CQ handles the defects and analyzes them to generate detailed reports for all the stakeholders, thus expediting the testing process by making it more efficient.
Superior test failure analysis Intelligent test automation and intelligent analytics help the testing and development teams to have a better insight into the impact of code changes and risks associated with those changes. Appropriate actions can be taken based on these insights and issues, if any, can be intercepted much earlier in test cycles.
Webomates’ patented AI Test Package Analyzer identifies all the test cases which are impacted due to a defect and traces them to impacted user stories/epics/requirements to identify the exact origin of the defect. This helps in understanding the root cause of the issue. For more details about test failure analysis, read our blog “Test failure analysis with AI”.
Read More about : Ai test automation
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