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AI Test Automation

AI Test Automation

Testing has always been the final hurdle which a software had to pass to be qualified as a quality product without having bugs, missing requirements or any errors.  To eradicate the time consuming and expensive procedure of manual testing which always requires human intervention, the AI based test automation is brought to use.

AI or artificial intelligence is the human like intelligence which is integrated with machines which enables it to function and mimic our responses and thinking. It is widely used in areas which needs humans to perform tasks such as problem solving and learning to improve the efficiency and performance. Now, integrating AI with testing can boost the capabilities of the testing process as it brings better software with minimal cost, time and effort. Reduced work load and precision in data output are some of the many perks that the AI based test automation provides.

AI Automation

Software testing is growing in importance and effectiveness with each passing day. With technological advancements which are more complex and interconnected with multiple technologies, AI based testing becomes relevant. To keep up with the Agile/Continuous Delivery, the testers need to be equipped with a solution that can ensure effectiveness and quality of software that needs to be released. With the help of cloud and SaaS, testing can be done remotely with the help of AI and Machine Learning. Curranty we have AI based cars which can navigate and reach the desired location, but the testing of software has only 20% tasks done by AI, while the remaining repetitive tests are conducted manually. The scope of growth to make faster and accurate testing to ensure deliverables of the complex requirements can be addressed using AI in testing.  Along with cost effectiveness AI powered testing results in improved reliability which ensures that the tested product will not alter any previously releases and automated tests can increase the faster cycle in deployments, thus reducing the need of fixing bugs after the deployment.

As the AI makes its mark in the industry as an effective time conserving and reliable tool, to maintain tests and test environment along with writing test cases for them might not be as cost effective as planned. Testers and developers are needed to ensure proper test cases are written for the AI to work as desired, with them the server and licence costs are also roped in for the maintenance charges.  AI based test automation involves upfront costs which is seen as cost effective anywhere 1-3 years after integration. The return of investment is directly based on the kind of the automation tools, work skill level and the testing resources involved. Companies like Apple, Facebook, Tesla, Google and many more rely and invest more on AI infused in testing to solve problems existing in the healthcare, autonomous cars, search engines and every area big or small which relies on AI to make business effective and flawless.

The AI powered automated testing is perfect for testing repetitive tasks that are done over and over again, and most commonly follow 5 main stages while testing. The stages of AI testing framework include learning from data sources, input data conditioning, machine learning and analysis, visualization and finally feedback. The data sources are normally detected and tested by automated data quality check, ability to handle heterogeneous data during comparison or by sampling and aggregate strategies. Input data conditioning is tested by data ingestion testing, knowledge of development model and codes, understanding data needed for testing and ability to create the subset and create the test data set. ML and analytics are tested using algorithms, system testing and regression testing methods. Visualization test is conducted using the end to end functional testing and API testing. The feedback is taken by Optical Character Recognition (OCR) testing, speech and image testing and chatbot testing frameworks.

The impact of AI based test automation is found in areas where there is need for organizations to produce products in the fastest time to the markets and expect error flawless end user experience. The necessity of technology in our professional and daily activities are evolving rapidly, and the dependency on mobile applications and home appliances cover every aspect of our user experience. Daily on a global scale these systems are used by the end user and expectations of a perfect system to be available without any shortcoming is expected by them. The majority of the business are developing applications using Agile rapid delivery framework which equated to approximately a new launch every 2 weeks. For optimal experience the businesses must ensure that before a deployment a proper testing must be done. The time span like this is simply inadequate for manual testing.

The future of AI bases testing looks very good in the industries as they depend on fast and accurate outcomes. In the future the automation bases testing is going to change the terms of risk based approach of software testing. As AI has the ability to learn different use cases and can create test cases based on real life user data. The reach of test coverage makes testing more powerful and effective.

To summarise the effectiveness of AI based test automation, the current businesses need to evolve to keep up with the market standards and expectation and this is only possible if the AI is integrated in testing process. By incorporating automation tools, the end result can be attained faster and the accuracy of the test case will not be compromised.  Change is the inevitable part of evolution, and AI based testing is the revolution that brings the change in market quality standards. Time efficiency, accuracy and cost effectiveness make AI based test automation necessary in business solutions.

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