Machines possessing the intelligence of human beings are helping testing teams to create quality applications. Such implementations are drastically decreasing regression burden, improving accuracy, and reducing time & effort.
AI (Artificial Intelligence) and ML (Machine Learning) are transforming the business landscape today. Introduction of these new technologies have immensely reduced manual effort, and their logical reasoning and predictive analysis are making them more reliable. Organizations are leveraging the power of AI and ML to reduce the complexity and tedious software testing processes. For instance, frequent regression testing may be prone to human errors due to its mundane and repetitive nature, but AI can eliminate the occurrence of human errors and improve the process efficiency.
The main goal of AI is to introduce a smarter and efficient way of testing that will improve the efficiency with its logical and problem-solving abilities. Here testing is performed by utilizing data and algorithm without human intervention. Let us understand how AI testing helps in creating quality apps.
Changing dynamics of app testing, how AI can help?
The automation market is transforming drastically; the automation which is being performed by writing codes/scripts is slowly moving towards leveraging the power of artificial intelligence of machines. This indicates the onset of the trend where we have slowly moved to adapting AI testing. The new development process heavy on regression requires an automated solution that will speed up the process, reduce manual effort, and implement logical reasoning while carrying out execution.
AI and ML can be implemented across a wide range of testing processes, using intelligent solutions. This approach will increase the application quality, offer seamless user experience, and improve the business revenue.
The role of tester in AI testing
Introduction of AI in testing has made creating, executing, and maintaining tests simpler, easier, and smarter. Further, the ability of the machine to self-learn and execute has created dilemma whether the role of tester is becoming obsolete. No matter how much technology might advance, but the role of testers will never disappear. Human intervention will be required to monitor and fix the glitches. Hence, the role of tester will still be relevant even with introduction of AI testing.
Creating Bots and training them to comprehend the environment and perform faster, intelligent, and smarter testing approach is a complex task. Bots should be fed with enough data, trained thoroughly, and monitored to ensure that they are performing the expected actions. The role of a tester is predominant in this scenario to feed data and train the bot. Further, the process should be monitored, and report should be analyzed by a tester to check if the application can be sent for deployment.
Scenarios in AI-testing that will require testers involvement:
- Gathering and feeding data
- Working on making the algorithms accurate
- Monitoring test execution
- Checking the bot’s response to input data
Hence, it is clear that human intervention in testing can never be replaced; however, there role will change with evolving technology. The mundane processes of execution and regression will be taken over by the machines, but the initial setup and report analyzes will always need a human presence.
*Benefits of AI testing *
The buzz that AI and ML are creating across industries is notable and underlines the immense scope this new technology has to offer. Leveraging the power of AI is found to be beneficial in application testing. It has helped organizations achieve greater efficiency in reduced time, cost, and effort. Here are some of the benefits of implementing AI testing:
Improved efficiency: AI testing ensures to identify any bugs and works towards creating bug-free application in reduced time. Hence, improving the test efficiency.
Better Test Coverage: AI testing effortlessly tests all data and internal programs with ease and accuracy, increasing the coverage. Effective test coverage can be achieved without human intervention.
Improved accuracy: Human errors can be easily eliminated by implementing AI testing. Machine with intelligence can identify bugs easily even under complex scenario. Hence, improving overall test efficiency.
Smart selection and prioritization of testcases: Smart Selection of test-cases involves the identification of the test cases used to test new release of an application based on previous data available. It is uniquely designed to upload data, train the model, and save any changes on real-time. For a given requirement, this model helps to find relevant testcases along with defect probability.
Reduced time and cost: AI driven test automation is the need of the time to ensure great quality without comprising on time, effort, and cost. AI test automation is intelligent enough to run the testing without the test cases, scenarios, and test steps. It removes all the tedious code creation, constant code maintenance, and scalability issues and speeds up the DevOps cycles.
Significance of historical data: AI test automation is designed to carry out predictive analysis of the historic data to predict the future behavior of the system. Generally, the testing models created based on the historic data will use the current data to check the possible results in the future, which will help in taking the required actions to optimize the outcomes as per the requirement.
These are some of the prominent benefits of AI in testing; it is to be noted that it is transforming the testing sector like never before.
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Conclusion
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As technology is advancing, organizations are also implementing AI and ML to stay in alignment with the changing business dynamics. This approach supports the modern developmental cycles like agile and DevOps and help them achieve faster and reliable results helping quick deployment and delivery.
The human-like intelligence of machines is allowing testing to be carried out in logical manner, not just matching the outcome with the expected results. Hence, AI testing is a powerful process that accelerated testing process and creates top-quality apps.
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