Significance of Machine Learning in Software Testing Methodologies

Posted By : Sanjana Singh | 31-Aug-2020

AI and Machine Learning in Software Testing Methodologies

 

 

Artificial Intelligence mainly focuses on the development of machines or techniques that can use intelligence to perform tasks in ways that reflect human intellect. Machine learning is a part of artificial intelligence. It can work with big data sets and then collect that data to perform testing and analysis in such a way that can find common patterns that are useful to observe the differences. Machine learning in software testing process can help support ongoing delivery during software updates and can significantly improve the time and resources used to create new software. Machine learning and Natural Language Processing are the forms of Artificial Intelligence that have been used to increase test automation.

 

Previously, in software testing, bugs and system vulnerabilities were only detected when testers entered certain parameters that required to identify the defect. Machine Learning has the ability to handle numerous issues as it moves beyond those limits and develops more complex test cases.

 

In this edition, we at Oodles, as a well-established Machine Learning Development Company, highlight the significance of deploying AI-powered tools in software testing. 

 

Beneficial areas of AI-Based Testing

 

  • Evaluating Unspecified Scenarios - Artificial Intelligence services are effective at tracking unspecified test cases or the test cases which are not related to the requirements.
     
  • Reduce Regression Testing Efforts - Artificial Intelligence interacts with the system to identify bugs or errors and hence remove test cases to minimize testing attempts.
     
  • Error Detection - AI can also show results by detecting whether a failed test case adversely affects the application or is simply the result of technical problems caused by the engineer.
     
  • Detecting the cause of the error - Artificial Intelligence can highlight possible reasons why a test case has failed to reduce the effort and time it has taken to determine the underlying cause of the failure.

 

Advantages of AI Bots

 

AI bots are not hard-coded and hence they can discover new features without breaking. AI bots can easily evaluate the modifications in the code whether they are new features or bugs of the application. AI bots have the ability to perform a number of experimental cases in a very short period of time.

 

Artificial Intelligence can identify and analyze even a very small modification in the application and organize them on the basis of their importance. Testing based on Artificial Intelligence can be easily adapted and implemented around all the new methods and features in the software application.

 

Natural Language Processing in Artificial Intelligence

 

Natural Language Processing uses the information of a variety of languages to create test cases that can be used in all stages of development of software. NLP has the capability of translating multiple coding languages to easily analyze and evaluate the test data. This expands the number of people who are able to work on the project and removes some of the complex, flawed parts of the process.

 

Conclusion

 

Artificial Intelligence comes up with the process to make the test easier and more accurate in a short period of time with clarity. AI data analytics technology is useful to get rid of the possibility of human errors and minimizes the testing time to detect possible defects. The use of Artificial Intelligence by testers is very helpful in improving the entire testing process, developing testers' technical skills, and hence contributes to the growth of the Organisation.

 

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