Machine Learning To Software Testing

Launchable Applies Machine Learning To Software Testing


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Launchable aims to offer intelligent testing and smarter DevOps. It also aims to remove all the tests that give slow or inefficient feedback. It promotes only those test cases that belong to a meaningful subset and lessens the delay in the tests that provide late feedback. It ponders over the fact that software projects require lots of testing to be performed. But performing all the tests could be wasteful for changing a small part of the big project. The software developers are aware of the fact that only a few tests need to be performed in the development of software, but they cannot determine with certainty what those tests could be because there are several tests with several purposes.

Machine Learning

Machine learning is a branch of Artificial Intelligence and Computer Sciences. It is trained for analysing data to identify the repetitive patterns and nature of a machine. It is used mainly for analysing, identifying and predicting similar patterns from a set of data by considering the occurrence of past events too. The engine of Machine Learning in the Launchable studies the occurrences, test results and past changes and makes decisions without much intervention from humans. This engine is trained by extracting information and test results from the data stored. 

The maximum advantage of Launchable can be taken by requesting validation and a loop for local development. It can also be used for performing intelligent tests for integration. For this, they are hiring beta testers. The beta testers are the user acceptance testers too.

We all know that software testing is a set of other tests performed to ensure that the software developed does what it claims to do. Hence, it is verified with its predicted outcomes by performing other tests that are efficient in testing its specific parts. Now let us understand what automated testing is. Automated testing is a software testing type that uses its automated software as a tool to test the functioning of the software before releasing it to the production environment. It is different from manual testing and can be performed any time a day.

Also read: About Machine Learning Algorithms

As mentioned above, the main concern for Launchable is the existence of many tests in software testing. The software companies have so many tests that they are confused and disoriented to use what test for which project. Sometimes some minute changes may take more time to be executed because there are several tests to be performed simultaneously. Unicorn companies like Facebook have taken measures to shortlist only those tests that take a shorter time for execution and feedback. This set of testing will be made permanent for their larger suite. This process is quite complicated. Launchable is working on this. To solve this problem, they are using AI to drive automated testing in the form of AI-driven test automation.

The use of the latest technology like Artificial Intelligence, Machine Learning and Blockchain is almost everywhere. It will not be surprising to have more robots and machines taking control over insanely human tasks. It is also predicted that other than these technologies being absolute leverage, they can also be the cause of disaster in the future where humans will be unemployed because most of their tasks will be performed by machines and robots. Even if they are employed, then it will be on the requirement of some really difficult skills that will require the humans to be as fast and as catching as the machines and robotic intelligence.

But in software testing, Artificial Intelligence and Machine Learning can deliver excellent and efficient automation that helps different teams by taking off their burden from many tasks. It makes those tasks easier for them. This also helps in software testing by providing improvised software quality and better testing experiences. 

Some of the applications of Artificial Intelligence and Machine Learning in the testing field are:

  • Ease in searching for errors and solving them
  • Taking maximum advantage of Machine Learning and user traffic in automated computing generation of tests.
  • Numerous apps are being developed. Some of them are fake and not worthy of trust. They crash and mess with the system of our desktops and mobile phones. Artificial Intelligence and Machine Learning are used in detecting and eliminating such mobile crashes.
  • They are also important for forecasting purposes. 
  • They provide an excellent monitoring service through their algorithms which also perform tracking.

Also read: Automation Testing Tools in Software Testing

This is how Launchable is applying machine learning to software testing. The existence of a lot of tests and the existence of fewer tests in different software companies has led to the use of Artificial Intelligence and Machine Learning in software testing. This will help all sorts of organizations and companies in shortlisting their tests for their software or products developed.

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