5 ways AI steals the spotlight on the stage of automation testing

Testing professionals have immense requirements worldwide. Testing is an endless career in Software field with greatest opportunities; each and every software has to be tested to assure its quality.

There is no doubt that mechanisation has made our lives easier and our tasks more efficiently. The idea of a machine with human perception has always been fascinating and made it a popular topic for most science Fiction. Today, this machine is no longer just a fictional object. Instead, it creeps and digs in slowly, but steadily, deep into the spheres of the human society. Aptly referred to as artificial intelligence, it is expected that they may think, imitate and perform, be even better than humans. From smart assistants and chatbots to simple email filters and custom response options, AI becomes an indispensable part of our everyday lives.

Automation Testing

Automated Tests have certainly improved test coverage, improved Test quality and reduced manual work. However, it is still not possible to achieve 100% automation as it requires extensive maintenance, which in turn requires human intervention. In such a scenario, merging test automation and artificial intelligence can create miracles for any organization. AI, along with machine learning, it is expected to be the driving force of the future of automation testing.

This blog speaks about 5 ways in which artificial intelligence steal the Thunder as testing is automated.

1. Deliver quality at speed

Test automation has already significantly reduced the amount of human aid. AI will further reduce it by limiting manual activities to tasks that are not feasible for a machine. The implementation of exploratory testing, monitoring and analysis of machine-identified anomalies, as well as the validation and correction of the decisions taken will be the main tasks that need to be operated by Hand. AI, on the other hand, will be responsible for activities that are too tedious and time-consuming to perform manually, such as searching for thousands of lines of code to detect anomalies and errors, identifying redundant test cases, deciding which test cases to run automatically, obtaining test coverage by extracting keywords from the requirements Traceability Matrix (RTM), and prioritizing regression test cases based on the application's high-risk areas. Unlike the Industrial revolution, when the machine was completely taken over and thousands of workers became unemployed, the AI revolution requires people and machines to work in tandem to achieve the best results. This symbiosis between AI and human is called Intelligence augmentation, which makes it possible to adapt the pace of development and to facilitate the timely release of quality software.

2. What do you mean? Stable, Agile testing with Cue-free maintenance

With the increase in demand for continuous delivery, it is imperative for organizations to introduce continuous testing procedures that are only possible through automation. As part of this process there are several Unit tests, API tests, and UI tests must be run regularly. Automated Tests automatically run test scripts. The maintenance of these scripts, however, had to be done manually, which, in turn, required enormous effort. This is where AI and ML will be very critical.

With machine learning algorithms, cross-Co-relations can be produced from all data collected. Based on this, AI understands what is normal behavior and what is not. AI is equipped with dynamic locators that detect even the smallest changes in the smallest element and make corresponding changes in the test scripts. This prevents test failures, guarantees the stability of tests, while test scripts are updated and maintained without human intervention.

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3. 3. 4. Self-healing mechanism eliminates scaly tests

AI can develop and improve on a regular basis through machine learning. It's like a photographic memory. It looks something and then never forgets it. It uses these observations to understand what is going on, what could happen next, and what it should do to mitigate potential risks, or to support upcoming actions.

Through a self-healing mechanism, KI can proactively detect and resolve threats before they occur. As a running process, AI keeps collecting data and feeding it to the ML algorithms. It helps to distinguish between the normal and abnormal behavior of the application, which triggers the self-healing exercise if necessary.

4. 4. 4. Removing physical module and server dependencies

If a test depends on responses or the implementation of certain modules, automation becomes a Problem. In the past, mock responses had to be created to successfully perform a test. Well, AI is self-sufficient. After a few manually created tests, AI can pick up and record the responses from a server. These recorded responses are then used for subsequent Test runs, eliminating dependency on a physical server or module. This in turn allows tests without obstacles or delays, and generates a high test efficiency.

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