QA Tutorials

Machine Learning in Testing

Machine learning

There are always new things to learn and keep our brains busy. Everyone likes to stay ahead for new development and it is considered as a new task, where we always have to look out for new trends. There are many trends in the testing industry. Everyone knows that smart software and machine learning has become a big part of our daily lives so it is not surprising that it also influences QA and testing. Now a days social networking use machine learning to mine personal information. Machine learning applies artificial intelligence which provide the systems an ability to automatically learn without human intervention. System as well as automation testing will improve and automate access of data, run tests and learn from results.

Why machine learning language?

Different tools such as machine learning, draw patterns from operations of data and enable the analysis of the heavy amount of data. It provides accurate results in less time and also provides effective way to test Internet of Things (IoT) solutions and many upcoming technologies. The different patterns will lead to the generation of synthetic and artificial test data which will improve test cases and testing in general.

Machine learning is helpful to engineers and everywhere to bring sense of the data. The Machine learning is useful because it reduces the time of programming. For example, if a software engineer wants to develop a program of correcting spelling and grammar correction, then after lot of efforts he will be able to develop a program. But by using machine learning tools directly he can develop that program with limited amount of time.

Another advantage is, it allows to customise the products to make it better like consider if a particular program is very successful and it is efficiently working and had a great demand and it has to be transferred in many different languages. It would take a lot of effort but by using machine learning one can easily collect the data of different languages and feed into machine learning tool. It helps in complete seemingly non programmable tasks.

We as humans has ability to recognise our friends faces and speech subconsciously but if anyone asks us to write programs then we cannot do it without the proper knowledge and also take time. But machine learning tools do it better. It properly identifies programs and machine learning changes the way we think about the problem. In more supervised machine learning, we first learn how to combine input, to produce useful predictions on never before seen data.

Terminologies in Machine learning language:

The terminologies we use in machine learning are:

Label: It is a variable we are predicting

Features: are the variables describing our data

Descending into Machine learning here we have Linear regression which is a method of finding straight line that best fits into set of points. There are lot of complex ways to learn from data, but we can start with something simple and familiar. Now we will consider how to reduce loss. The different hyper parameters which are termed as configuration settings used tune how the model is trained. Derivative of (y-y2) with respect to the weights and biases tells us how loss changes for a given example from simple to compute and convex. So repeatedly taking small steps in the direction that would minimise the loss. These steps are called as gradient steps.

Machine learning includes lot of components like Data collections, data verification, machine resource management, feature extraction, Analysis tools, serving various infrastructure, process management tools, configuration and monitoring which are used predictions for a different world. We can re-use generic machine learning components wherever possible instead of building application by ourselves and components can also be found in other platforms like spark, Hadoop etc.

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