Before getting our hands into the comparison, let’s be clear about the traditional test automation first.
Traditional Test Automation
Test automation is the evaluation of the software product automatically. During this evaluation process, it is ensured that the software product complies with quality standards and provides expected functionality and business logic, while preserving the high quality user experience.
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. The unit testing is usually performed by developers.
Integration testing is performed to ensure that various parts of the code can be integrate and work seamlessly without producing undesirable results.
System testing validates the compliance of the software product with the requirements as a whole system. It is a process consisting of load, security, performance, and reliability tests.
Acceptance testing is carried out to determine whether the product meets the requirements that are determined by the users or customers. Here, users and customers are again performing the test. Sometimes 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 the DevOps practices, the whole process is now automated. Well, you have an idea of what is test automation now!
So.. What is the AI based test automation?
AI Test Automation
The AI practices help accelerate complex processes and make sense of them more thoroughly so that in today’s world, it is not a big surprise to easily run into artificial intelligence in software applications.
Although artificial intelligence practices for test automation are a new concept, there was a definitely progress over time.
AI consists of a concept based on mimicking human behavior. The workload created by traditional testing methods and the human resource required to handle it is too large to ignore. Of course, this varies according to the software product’s industry, scope, and function.
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.
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. It saves time for test automation engineers because the human resource required is relatively less, and it can produce faster results by using the power of data. It provides a significant benefit by increasing the product quality in a short time. Such vehicles are generally flexible and can adapt to any conditions.