It is safe to assume that testing, as it is known today, will change because of ML tools. But even when it seems possible, there are still many concepts to analyze before giving a straight verdict.
Author: Gabriela Ponce
Over recent years, Machine Learning has been a trending topic in software development. Because of its inherent characteristics, there is speculation regarding these types of technologies taking over traditional testing roles. But even when it seems possible, there are still many concepts to analyze before giving a straight verdict. Let’s start with some basics.
What is Machine Learning?
Simply put, Machine Learning (ML) is the science of getting systems to learn and act by receiving and analyzing data. This means applications that implement this technology, through the use of learning algorithms, can predict plausible scenarios or make accurate decisions.
There are different strategies to get ML systems to learn. The choice would depend on what the goals for this system are and the type and amount of data available. Therefore, building an ML system involves an unavoidable phase of training, in which the algorithms are induced to different scenarios and data. This way, it can learn from the given experience and produce accurate predictions.
As these systems can acquire patterns according to the provided data, it seems just a matter of time for them to also learn how to predict the errors a system may produce.
Does that mean machines can test themselves?
It seems highly possible that complex systems could learn to identify defects. As these systems can process a huge amount of data, it is expected for them to deliver a more precise analysis in less time than a quality analyst. This makes them ideal to be implemented for the repetitive task as an automation aid. There are already some AI testing tools, such as Appvance, Mabl and Testim, that use ML to analyze an application and create automation scenarios by themselves.
But the applications for ML on testing are not limited to automation. These technologies can also be used to evaluate and create traditional test cases based on recollected data, as test.ai presents. Moreover, performance and load testing could be optimized by introducing a comparative analysis that provides useful insights.
For now, these tools could fulfill the assistant role for quality analysts, and it is a growing trend. Therefore, it is a logical assumption that these could grow so complex and sophisticated that they are able to replace the analyst for good.
However, this is a fairly new approach to software testing, and there is still a lot to learn about how these types of technologies can be implemented.
This might be interesting: Software testing: is the functional tester doomed to disappear?
Should quality analysts be worried?
It seems natural to assume that, in the near future, ML technologies applied to quality assurance could easily take over manual testing roles. But even when some testing duties could be replaced by complex ML systems, this could also be an opportunity for quality analysts to improve their skills, meaning that testing tasks would become more sophisticated and challenging.
For example, it is highly possible that the most basic flows on a functional regression could be automated. This will push the quality analyst to find more complex scenarios to detect errors in which creativity and knowledge of the system and the used technologies become crucial.
Also, it is important to point out that automation testing is more likely to be introduced when an application is in a mature stage. Therefore, the manual tester would still be needed in the initial phase of the development cycle. It is also worth mentioning that some specific types of testing are still more effective when performed by a human, like exploratory testing.
Having said that, the quality analyst will still be relevant in software testing, but there will be a demand for them to develop new skills. It is important to consider that ML tools also need to be tested.
What can quality analysts do to stay up to date?
In the age of ML tools (and AI tools in general), it is safe to assume that investing time on learning ML testing strategies can be useful. This would help the quality analyst to understand what the possible wrong assumptions a system can make are.
In traditional roles, a deep knowledge of the specific business logic can be helpful when trying to find new scenarios that could produce bugs. But that is arguably a result of experience on a specific type of business.
Furthermore, being involved in testing non-functional requirements, such as performance, security, and usability, provides a useful background for the quality analyst to become a more essential part of the team.
Although in the big picture, there is no method or checklist to calculate how valuable and sustainable someone’s work is. It would highly depend on the quality analyst’s initiative to develop new skills and not let them become outdated.
A tester must always be learning and implementing new techniques to find system failures and provide feedback to the team. This is aside from ML tools, and this could vary according to specific interests.
To sum up
It is safe to assume that testing, as it is known today, will change because of ML tools. In view of their implementation, it is expected that these technologies will somehow make traditional testing roles become obsolete.
However, for the time being, it is still unrealistic to assume that the human factor will be completely disregarded. Being that ML testing tools are so recent, there is still much to learn about them, and some may not even be reliable. Moreover, it is still expected that manual quality analysts are involved in early stages of the development, as some types of testing are still more efficient when performed by humans.
Still, the quality analyst will be expected to have a deeper technical background and work with and alongside ML testing tools to ensure the quality of the product. There is time for them to deepen their knowledge and acquire experience to define new strategies.
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