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Machine Learning in Testing

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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14 Comments

  1. Social networking uses machine learning to keep all personal information safe. Machine learning applies artificial intelligence which provides the systems an ability to automatically learn without human intervention. A software engineer can develop the program in a short amount of time using machine learning instead of developing a program the traditional way.
    Label: Variable that we predict.
    Features: variables describing our data.
    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 for predictions for a different world.

  2. In todays social networking world there is a wide use of machine learning which in turn applies artificial intelligence which helps to automatically learn without any human intervention. It is helpful to engineers and developers as its a time saver and almost error free.

  3. Machine Learning in testing can help speed up the testing, with artificial intelligence able to mine from huge test results/data , in generic scenarios.

  4. 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.

  5. Machine learning applies artificial intelligence which provide the systems an ability to automatically learn without human intervention.
    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.

  6. Machine learning (ML), which has disrupted and improved so many industries, is just starting to make its way into software testing. Heads are turning, and for good reason: the industry is never going to be the same again. While machine learning is still growing and evolving, the software industry is employing it more and more.

  7. 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. Machine learning is useful because it reduces the time of programming. 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.

  8. Machines learning is a study of applying algorithms and statistics to make the computer to learn by itself without being programmed explicitly. Computers rely on an algorithm that uses a mathematical model.

  9. 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.

  10. Everybody 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. Nowadays a social networking use machine learning to mine personal information. Machine learning applies artificial intelligence which provide the systems as ability to automatically learn without human intervention.

    Advantages of machine learning language:
    1. Different tools such as machine learning, draw patterns from operations of data and enable the analysis of the heavy
    amount of data, and provides accurate results in less time.
    2. ML is helpful to engineers and everywhere to bring sense of the data and is useful because it reduces the time of
    programming.
    3. ML allows to customize 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.

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