LEVERAGING MACHINE LEARNING FOR QA TESTING AND SOFTWARE DEVELOPMENT

10Jul - by aiuniverse - 0 - In Reinforcement Learning

Source: analyticsinsight.net

Developments in machine learning are significantly supporting QA Testing and Software Development process

The advent of DevOps paves way for businesses to actively look for real-time risk assessment backed by machine learning algorithms throughout the various stages of the software delivery cycle. QA engineers face a plethora of difficulties in the juggle to find out a perfect solution. At the time of testing, it becomes quite a task to make new additions in the existing code which has already gone through the testing process. Every time there is an expansion on the existing code, the development team must carry out new tests. While regression testing cycles can be time-consuming, undertaking them on a manual basis is bound to overwhelm QA engineers.

Software development lifecycles are becoming more complicated with each passing day backed with reducing delivery periods, in this scenario testers need to impart evaluation and feedback on a live basis to the development teams. The swift pace of new software and product launches leaves no other choice for product development teams to test smarter, not harder in this day and age.

Assimilating Machines to Mimic Human Behaviour

With the advent of Artificial Intelligence and Disruptive Learning, testers team can move beyond the traditional route of manual testing models and progressively march towards an automated and precision-based ongoing testing process. What is the advantage of leveraging AI for machine learning? An AI-powered continuous testing platform can observe even the slightest changes to algorithms more efficiently than a human. In modern technology parlance, AI is widely used for automation testing in object application categorization for all user interfaces. The QA engineer recognizes controls into categories where testers can pre-train controls that are commonly seen in out of the box setups. Once the hierarchy of controls is observed, testers can prepare a technical map such that the AI model looks at the Graphical User Interface (GUI) to obtain labels for the different controls recognised by the QA engineer.

Testing assimilates the verification of results; which translates accessing to a plethora of test data. Interestingly, Google DeepMind created an AI program that uses deep reinforcement learning, to play video games by itself which gives an abundance of test data. Artificial Intelligence will let users perform exploratory testing within the testing site, using the human brain to assess and identify applications under the testing process. This will lead to data scientists to automate test cases.

The Case for Automated Testing

Assessing user behaviours means assigning, monitoring, and categorizing the appetite of risk preference. This data is a classic case for automated testing that lets data scientists evaluate and remove anomalies. Heat maps help to identify bottlenecks in the process and to help determine which tests data scientists need to conduct, and which redundant test cases and manual tests can be automated. This way testers can, in turn, focus more on making data-driven connections and decisions.

Continuous Deployment

Continuous integration puts great emphasis on testing automation that assures new commits to one module do not break other parts of the application when it is integrated into the main branch. Continuous delivery extends continuous integration to make possible a quick and sustainable release of new changes to customers. Continuous deployment is a real-time deployment and is an excellent way to accelerate the feedback loop with customers.

Agile Software Development

Agile software development exhibits some similarities to Waterfall, V-Model, and Iterative, but asserts substantial improvements in workflow and management. Agile software development is so popular that it embellishes the familiar meaning, and describes an approach to software development which makes collaboration an imperative, generating self-organizing cross-functional teams among developers and customers.

Ultimately, risk-based automation assists users in determining which software development and QA tests they need to run to get the best desired results. With the amalgamation of AI in test creation, execution, and data analysis, software developers and QA testers can permanently do away with the need to update test cases manually and instead identify controls, spot links between defects and components far more efficiently.

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