The next big game changer

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Technology has been the greatest game changer of our time and has become an integral part of our day-to-day operations. The evolution of technology has important implications for our lives, both personally and professionally. We have all become very dependent on technological solutions that make our lives and work flow easier. Many tech enthusiasts, as well as tech companies, are curious about what the next big tech disruption will bring and how it will further disrupt our lives. Artificial intelligence (AI) is one of the main game-changing elements today.

AI does not require any introduction, because each of us uses some form of AI in our daily life. From a business perspective, almost all organizations with an online presence today operate with active AI mediation. However, in layman parlance, artificial intelligence is nothing more than the replication of human intelligence which is processed by high-tech machines and is programmed to function like humans and mimic their actions for the purpose of reduce or minimize errors as well as free up human time. to make additional tasks more valuable.

AI helps businesses by minimizing manual tasks and automating many processes that require human intervention. But as we move forward, it becomes crucial to keep abreast of the latest developments in the constantly changing world of technology. Today, many companies are using AI in software testing because it is a crucial practice that ensures customer satisfaction within any service they use, whether it is from an app or website. Additionally, routine AI-based testing can protect potential software failures that could otherwise be disadvantageous to businesses in the long run. Thus, many companies are planning to set up or have already set up AI tests, to evaluate and scrutinize products, platforms, services, etc. respective ones, in order to become aware of the risks and the failure threshold involved in its execution.

Implementation of artificial intelligence in software testing

The ever-changing digital ecosystem demands that applications be reliable and flawless, making application testing a crucial part of business strategy. In such a scenario, the quality of the application matters most, in terms of performance, features, and usability that sets businesses apart from others.

KPMG, a global leader in providing auditing, tax and advisory services, in one of the industry articles, said that “AI helps evaluate business-critical algorithms, sets up of test controls and oversees the design, implementation and operation of Al’s programs. to help meet the inherent challenges: integrity, interpretability, fairness and agility ”. Thus, it is of the utmost importance that companies implement AI in software testing of respective services and products.

To ensure better quality of service, companies based in India are slowly embarking on the process of using AI in software testing and development. However, the practice is still in its infancy and the amount of autonomy required to reduce manual effort is at an all-time low. Nonetheless, organizations are realizing that automating manual test processes with the right tools and the right technical know-how is the need of the hour, to speed up and improve the test cycle and thereby increase return on investment. .

Here are the top 3 trends for testing using artificial intelligence

  • Self-healing tests: With self-healing, any changes in ownership are automatically detected and internal scripts are self-healing during a run. Today, in reference to User Interference (UI), self-healing testing is no longer far-fetched practice but a reality. Sometimes technical issues occur when an object / product changes location, here self-healing testing kicks in and makes the UI locator more functional in terms of identifying the path analyzer , which is not likely to happen otherwise during the mobility of the object. There are more developments underway in the self-healing space and companies could look forward to further innovations in the coming days.
  • Intelligent Automation Execution: Whenever there is a risk of application failure or production failure, based on the previous data, the AI ​​engine promotes the code or highlights features to the using intelligent algorithms that decide whether the code can be processed or not. Additionally, to reduce the risk of application crashes or failures, AI might recommend using specific methods from the code repository. To automate quality doors, automated code repositories are maintained for different projects based on their individual success rate.
  • Autonomous Testing: AI, specifically, machine learning has been a game-changing practice when it comes to testing standalone software, as it can spot errors and perform build, eventually speeding up case development. product testing. In recent years, identifying bugs with multiple iterations has been a time consuming and expensive task. Automation testing has virtually improved overall test coverage and application performance, as solving technical issues takes less time and is faster and more accurate, saving a lot of time and money.

In conclusion, to cope with the new age business changes and to achieve the stated goals in terms of technological aspects, companies must implement AI for testing, with the aim of running business operations smoothly, because the more applications we use, the more it is necessary to test them regularly, to provide internal and external stakeholders with a seamless experience in doing business.

By Niraj Hutheesing, Founder and CEO, Cygnet Infotech


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