Explore the Potential of AI-Driven Automated Testing
The success of any software development project depends on the people that execute it. Today, project managers and software engineers face several challenges while developing and deploying software solutions or services.
Quick turnaround and accuracy sit at the core of deliverables resulting in high-pressure environment for the team. In addition, frequently changing scope of work and pressing demands for flawless execution further adds to the dilemma.
The evolution of smart technologies has significantly lowered the rate of technical difficulties countered during project development leading to reduced product delivery time. This has, in turn, improved user experience while integrating compelling innovations.
Artificial Intelligence is here to make businesses far more competent and well-organized. Market leaders have begun using AI to boost user experience and client interactions. It has the potential to transform all surfaces of test automation by streamlining development, implementation, deployment and maintenance. It can also provide actionable insights in real-time that pinpoints the root cause.
The huge volume of data collection and advancements in algorithms have extended the scope of software testing. Now, it has become possible even for non-technical users to outline and perform tests with a higher level of precision.
Why Enterprises Need AI-Driven Automated Testing
AI-driven Automation Testing is applicable to a variety of platforms, frameworks, APIs and applications such as web apps, mobile apps, backend services, IoT solutions, websites, test types, function, performance, security, etc. The impact of AI within enterprises is going to be huge and entrepreneurs are quite excited about upcoming enhancements that AI will offer.
“About 84% of enterprises believe that investing in AI will lead to greater competitive advantage.”
Automation Testing provides the ability to build and execute tailored tests based on the requirements. By doing these tailored tests for applications, developers and testers can forecast the issues that may arise. These tests themselves can be improved by learning from the results or changes of applications. Consequently, testers will always be working on complex issues, rather than testing applications manually.
Automated testing does not mean it will save time for testers; it will just change the way time is used by testers. During legacy testing, testers will mostly test and manually search for problems and report them in a repetitive process, while with AI testing, they will build the tests themselves.
Tailored tests created in AI will allow them to collect optimized data. Automation Testing Services powered by Artificial Intelligence provides metrics that have detailed execution cycles, success rates, active runs, etc. Such analytics can help developers and testers understand the opportunities that can be addressed when perfecting the software.
By implementing QA processes using AI-powered Automation Testing, QA teams can:
- Automate quality gates: The AI engine promotes the code or blacks out the features with high chances of causing application breakdown or production faults using intelligent algorithms that determine if the code is good to go or not based on historical data. Automated code repository can be maintained for different projects based on their individual success ratio to automate quality gates. AI can make further recommendations to use a specific method from the code repository which will reduce the chances of defects.
- Forecast root causes: One of the reasons for delays in releasing new features is the identification of the root cause of a defect. By monitoring patterns and connections, ML algorithms can suggest defects to root causes, along with the AI performing remedial tests before the code develops further. The scope of error can drastically reduce while AI makes these decisions.
- Influence precognitive monitoring: Machine Learning algorithms lookout for indicators in coding errors that were ignored in the past. The algorithms can then flag these indicators, for example, a high memory usage can be flagged as a potential threat that can result in an application failure. The AI engine can automatically power up a parallel process to enhance the server-resource consumption as a curative step. Apart from this, AI can also suggest the automation scenarios to be executed based on new feature implementation and past defects data.
- Makes unified QA process: AI manages the array tools in advance based on an upcoming requirement and unblocks the delivery pipeline. This makes it the best-fit for an Automation Testing tool based on historical data as it enables seamless continuous QA testing. The QA team will have a ‘unified’ functional, performance and security testing for smooth process through AI scripting and codeless test creation.
- Ensures cost optimization: AI is modernizing the authorized testing tools, advance basic functions and help project leaders optimize additional testing tool costs.
Businesses who strive to become market leaders need to start exploring the vast potential of Artificial Intelligence. At Cygnet Infotech, we believe the future is all about AI and Machine Learning technologies. Cygnet has always been ‘Technology First’ - adaptable and flexible in order to serve customers irrespective of their domain.
Nilima Shah is a senior content writer at Cygnet Infotech. She enjoys researching, reading and writing articles about the latest Tech Buzz. She believes in using her skills both at a professional and on personal level to encourage businesses to adopt the latest technologies through her engaging write-ups. In her free time, she loves to create DIY art and crafts, explore places and cuisines.View All Posts