Differences Between the Traditional & AI Based Test Automation
In the ever-evolving world of software testing, it is crucial to understand the critical differences between traditional test automation and AI-based test automation. While traditional methods have served us well, the advancements in artificial intelligence have paved the way for a new era of test automation that offers unparalleled efficiency and effectiveness. In this post, we will delve into the contrasting aspects of these two approaches and highlight the transformative potential of AI-based test automation.
Traditional Test Automation
Traditional test automation has been the go-to approach for evaluating software products against quality standards and ensuring expected functionality. This process typically involves four stages: unit testing, integration testing, system testing, and acceptance testing. While effective, traditional test automation can be time-consuming and costly as the number of test cases decreases with each stage.
Test practices generally consist of 4 stages. After each stage, the number of test cases decreases while the test execution cost is increased.
- Unit test
- Integration test
- System test
- Acceptance test
To give a brief and clear summary:
Unit testing is the testing process of a separate and specific part of the code (for example, functions). After the process, it is validated that the tested unit gives a correct output and works as expected. Developers usually perform unit testing.
Integration testing ensures that various parts of the code can be integrated and work seamlessly without producing undesirable results.
System testing validates the compliance of the software product with the requirements as a whole system. It consists of load, security, performance, and reliability tests.
Acceptance testing is carried out to determine whether the product meets the requirements determined by the users or customers. Here, users and customers are again performing the test. Stakeholders and/or related business units can also participate in this process.
Testing the whole application manually while covering the above steps is not cost-efficient and feasible. Fortunately, with the test automation codes created by test engineers combined with DevOps practices, the whole process is automated. Well, you have an idea of what test automation is now!
So.. What is AI-based test automation?
AI Test Automation: Embracing the Future
Today, most organizations are moving forward with agile working logic. The “agile” expression, just like its meaning, expresses agile and rapid software development and testing iterations. Therefore, it is necessary to continue to develop products that are as efficient and bug-free as possible by completing the iterations healthily and securely and providing appropriate maintenance of these products when required.
Artificial intelligence brings a revolutionary perspective to the realm of test automation. By mimicking human behavior and leveraging data-driven insights, AI-based test automation accelerates complex processes and enhances the overall efficiency of software testing. With AI, the need for extensive human resources is significantly reduced, allowing faster results and increased product quality in a shorter timeframe. In today’s world, it is not a big surprise to run into artificial intelligence in software applications easily. Although artificial intelligence practices for test automation are a new concept, there has been progress.
When we combine the above statements, the traditional test automation methods can be slow and insufficient in terms of cost (human resources & time) and usually cause a decreased software product quality.
Fortunately, this gap can be closed with AI-based test automation.
Bridging the Gap: Benefits of AI-based Test Automation
- Enhanced Speed and Efficiency: AI-based test automation eliminates the bottlenecks and limitations of traditional methods, enabling faster and more efficient test execution. The power of AI allows for quicker identification of critical test scenarios and the generation of automated test cases, saving valuable time for test automation engineers.
- Increased Product Quality: Traditional methods can sometimes need to catch up regarding cost-effectiveness and maintaining high product quality. AI-based test automation fills this gap by leveraging advanced algorithms to identify potential issues, optimize test coverage, and deliver more reliable and comprehensive test results.
- Adaptability and Flexibility: AI-based test automation tools are designed to adapt to dynamic software environments, making them suitable for agile development methodologies. These tools can handle evolving requirements, changing test scenarios, and varying software landscapes, ensuring a robust and scalable testing process.
Unlock the Power of AI with Cunda
Cunda, The Next Generation AI Test Automation Platform, is at the forefront of this transformative shift in the testing landscape. By harnessing the power of AI, Cunda empowers organizations to migrate from slow and insufficient traditional methods to fast and powerful AI-based test automation. With Cunda, you can experience accelerated test execution, improved test coverage, and enhanced product quality.
Conclusion
Adopting AI-based test automation is no longer a luxury but necessary in today’s agile-driven software development landscape. By embracing AI, organizations can bridge the gap between traditional and AI-based test automation, unlocking unprecedented speed, efficiency, and quality in their testing processes. Leap AI-based test automation with Cunda and experience the future of software testing today.
Explore the possibilities and benefits of AI-based test automation with Cunda, and revolutionize your testing efforts like never before. Don’t get left behind in the fast-paced world of software development — embrace AI and take your testing to new heights of excellence.
