When the first computer bug was discovered in 1947, it was quite literally a moth that had become trapped inside a system at Harvard University that was disrupting the electronics. At that time, the concepts of debugging and testing were indistinguishable. But in 1957, testing became its own separate concern as it looked into how an application performed in real-world scenarios.
Testing continued to evolve from its humble beginnings. From the late 1970s, testing started to look beyond just functionality—it looked at ways to break the software, uncover errors and test its quality. By the 1980s, testing tools such as AutoTester were developed to make testing even easier. At this point in time, there are all sorts of automated testing tools for all types of applications.
Recently, though, artificial intelligence (AI) has entered the scene. This has led to a distinction between traditional automation testing that came before and AI, which is rapidly becoming essential.
Traditional Automation Testing
Traditional automation testing involves manually implementing various tests, including unit tests, integration tests and regression tests. Even with powerful tools such as Selenium, Appium and Katalan, it is still a time-consuming process.
There are several advantages, though, to using traditional automated testing. One advantage is that there are already established methods. That means that testers already know what they have to do to successfully test software. In addition, there is a lot of community support. If a tester runs into an issue, it is likely that they can ask the community and others will have had similar issues that they can give advice on.
Unfortunately, traditional automation testing also has its disadvantages. One problem is that it requires high maintenance. Many human testers are needed to keep a traditional automation testing environment running optimally. Another issue is a lack of flexibility. If a change needs to be made to a test, it can take a lot of time to make that change in scripts.
AI-Driven Testing
AI-driven testing uses machine learning algorithms to mimic human intelligence while performing software testing. It can perform the same functions as traditional automation testing, such as test generation, test execution and results analysis.
Using AI for testing can have distinct advantages. We can see that it is more efficient than traditional automation testing. That is because it can perform the work of multiple people in only a fraction of the time. It is also apparent that AI is highly adaptable. That relates to AI having the human ability to recognize shifting requirements and changing test scenarios. One more advantage is its capacity for self-improvement. AI can learn from each test execution, so it is constantly reacting and trying to improve itself.
As with traditional automation testing, there are also downsides to AI-driven testing. One disadvantage is its complexity. When an AI model gives undesired results, it can be very difficult to troubleshoot because AI is a bit of a “black box.” Another issue is its higher initial cost. It can take a lot of resources to properly train the AI, and the training materials need to be expertly selected. One further issue is the specialized skills required for AI-driven testing. Integration of AI into an existing workflow can require deep knowledge of both the existing process and AI.
Just like with traditional automation testing, AI has both positives and negatives. Let’s now move on to comparing both of them side by side.
Comparing Both Approaches
When deciding on whether to use traditional automation testing or AI-driven testing, it is important to consider the needs of the project. Let’s consider each type with regard to costs, resources and implementation time.
Regarding costs, traditional automation testing has a lower up-front cost. Testers and the tools they use have a predictable cost that does not change much over time. On the other hand, AI costs a lot more to implement at the start. Over time, though, the operating costs of AI-driven testing should decrease as there will be less manual labor involved.
In terms of resources, traditional automation testing requires small teams of experts. They need to be skilled in various programming languages as well as automation frameworks. For AI-driven testing, a larger team may be needed initially to build up the data set and work with the AI. They will need to be familiar with both automation processes as well as AI.
With regard to implementation time, traditional automation testing has a faster startup time as the team can follow familiar processes. Maintenance, however, can involve making changes to scripts, which can be time-consuming. AI has a longer build-up time as the system is trained. However, AI’s ability to learn means long-term maintenance will be much faster.
For projects that need to be set up quickly with a low up-front cost and small team, then a traditional automation testing method is preferred. But if the project is a long-term one and a company can invest in higher up-front costs to see more benefits down the road, then AI-driven testing is a good choice.
Future Trends
AI is moving testing in a direction where expert testers can use it as a copilot to make them more efficient at their jobs. This will allow current testers to be able to take on more jobs, which means more software will be better tested than ever before.
Conclusion
A lot has changed in the field of software testing since its earliest beginnings of finding a moth stuck in a computer at Harvard. The recent movement toward AI is another step in the endless evolution of testing.
Hopefully, I've been able to convey that there are numerous differences and strategic implications for using both traditional automation testing and AI-driven testing.
Implementing AI is a process that should be encouraged to further explore the limits of modern testing. A future where testers can leverage their expertise alongside AI will definitely be an exciting thing to behold!
