The integration of Robotic Process Automation (RPA) with Artificial Intelligence (AI) has managed to revolutionize business process automation. It's led to improvements in operational efficiency and pioneered the establishment of new standards for test automation. By...
In the age of DevOps and continuous testing, automated testing has been at the forefront of everything we do. As applications become more complex, it’s even more important to shift testing left to find defects early, fix them, and release updated features as fast as possible. Automated testing is one way to help keep up with demanding project schedules and release cycles.
The common question that arises when companies are trying to automate everything is, will manual testing be needed at all?
The short answer is that manual testing will still be needed, but the real answer is more complex, because those testers’ roles will be changing. Here are four ways manual testers can prepare for the transformation.
1. Focus more on exploratory testing
Does your team develop detailed test plans and then execute them to ensure the system is working as expected? This is an effective approach with less complex systems where the degree of randomness in the outcomes is minimal. But with technologies such as AI and blockchain, it is hard to predict an outcome based on the given inputs. Several external stimuli may affect how the system works, and it is impossible to think about all the unexpected outcomes to test for and detail them each in a test plan.
For example, AI-based systems are generally a black box. We do not control or understand how the AI model forms different relationships or makes decisions. We provide different training data sets and monitor the system’s learning and progress. We are trying to make predictions on future values based on learning from past examples or discovering different patterns from data sets.
A premade test plan is not going to be helpful in this scenario, as the outcomes and decisions of AI-based systems are unclear. The approach to test these systems must be adapted to focus more on providing diversified data sets and evaluating the decisions made by the AI model against specific metrics to determine performance.
Consequently, manual testing will be needed to investigate different areas of these complex applications. Testing will be more focused on exploring the application than on following predefined steps.
2. Build on previous experiences
With the rise in new technologies, frameworks and tools, there is often a misconception that the previous experiences testing applications is no longer useful. This is not true. For example, based on testing web applications, you already know some of the common problems that may occur when customers use the website.
The takeway here is to learn as much as you can about your application, including how your users interact with it — as this knowledge will continue to be valuable in the future.
3. Prioritize continuous learning
It is important to learn how new technologies work and impact end-users. You don’t need to know everything about every technology, but at least understand the basics of how they work so you can test systems more efficiently.
For example, if your company is implementing a blockchain framework like Hyperledger, it would be helpful to first understand how blockchain frameworks work and then learn how this framework is implemented within your systems.
Continuous learning is going to be important for not only manual testers, but anyone trying to make sure the systems work as expected.
The good news is that there are a variety of ways to learn a new technology such as React, from online tutorials, video tutorials on platforms such as YouTube, podcasts, books, and free online courses.
4. Experiment with different hats
Testers will have to collaborate with developers, business representatives, stakeholders and infrastructure teams and be the glue that holds everything together. Apart from exploratory testing, they will also spend a considerable amount of time acting as information brokers between different teams, giving new insights about the system based on their exploration. They will begin serving as domain experts.
So contrary to popular belief, the future of manual testing is bright. The role will continue to evolve and be a vital part of the testing process, and there will be more opportunities for testers to exercise their creativity and explore the application.
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