Performance testing is also popularly known as “Perf testing” and comes under the performance engineering field. It focuses on non-functional t...Read More
Every other day new technologies are emerging into the market making life easier, faster and smoother. Artificial Intelligence and machine learning are technologies which are now a part of our lives. In day-to-day life, we use Smartphones, electronic cars, drones, which are boon from AI. Apple’s Siri and Amazon’s Alexa have become part of our family.
Machine learning origin dates back to 1952 and its data-driven approach came decades after in the ’90s. Machine learning is to learn computer manipulations and extraction of data. It emphasizes developing programs that have the capability to access their own data. It is autonomous and has given birth to modern AI.
So evidently we already are using ML in our daily life, so now the question arises how ML is useful in testing?
When everything is going so fast we can expect obvious complications in the software lifecycle like:
Testing manually is not feasible in the above-mentioned scenario. So the smarter choice is to adopt the technologies that help us to pace up with the change. Software testing will advance with AI and ML. Many top most companies like Apple, Amazon, and Facebook already have started using machine learning applications.
In Facebook’s case, it helps in getting the data like what type of content users want, like and how often they communicate with the world.
Earlier programmers and developers had to input code. The computer would carry out instructions according to the language used by the developer but still coding is done and is necessary, but the way developers interact with systems is different, at least when it comes to machine learning.
Now, developers act more like trainers, guiding the system and offering tips or advice in the form of objects and play with them which enforces the system to carry out the thinking and work to achieve desired goals. It may sound crazy to describe a machine or computer as a “thinking” thing, but it’s true. The computer can tap into an endless supply of data to piece together everything it needs then make decisions and hit the goals (“bull’s eye”).
In most cases, the way in which the system figures out an answer is a mystery. Many Applications or autonomous machine learning platforms exist with pre-built testing techniques incorporated already that carry out steps themselves. The development team knows what’s happening on a basic level, but may not truly understand what the software is doing behind the scenes to find the answer.
The magic lies here – The machine or platform in question is able to not only automate but influence the way in which testing takes place. At an instance, it knows there is a defect, but more importantly what may be causing it. It may also be able to make suggestions to remedy the problem in real time or even have the capacity to go fix the problem on its own.
Machine learning is capable of parsing time-consuming tasks and change the development and testing phase into a more convenient experience for developers. Of course, it’s going to take some time to perfect the systems and backbone that can do such a thing, but we are getting an inch closer every day.
Software tester has nothing to worry about the introduction of AI instead they have to think on how to incorporate AI and ML. With the introduction of ML, we still need testers for is execution because AI lacks some important types of checks like scalability, performance, documentation, and security.
We already know the ability of AI and ML in the past and what it can do in the future. The software industry, in particular, will see a lot of changes. As per my experience, I have seen when any change is going to happen we always create some illusions of it but never think of accepting such changes.
Now if AL and ML will be introduced in software testing. We have to create a mindset on it how we have to work with it, what is the skill set required to upgrade ourselves.
Breakthrough in these technologies will be worth many Microsoft’s and Amazon’s. Artificial Intelligence and Machine Learning will broaden our horizon and opportunities. Now, this might be a question in everyone’s mind will Manual testing will be overtaken by AI and ML?
The answer is: ‘NO’. Both will coexist because manual intervention is required for designing test strategies. Software testers need to build its data science skills and should be able to understand how Machine Learning works.