When it comes to utilizing AI in software testing, efficiency, speed of execution and greater accuracy is where AI stands out. In this article, you will get to know how AI and ML are going to impact the future of software testing. Read more https://testingxpertsuk.blogspot.com/2022/07/how-ai-ml-are-going-to-impact-future-of.html

1. CONCLUSION
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The platform of Artificial Intelligence (AI) and Machine Learning (ML) is evolving at a rapid pace and is gaining huge
momentum in the IT industry. There are already many industries that are getting immensely benefitted through the
strategic implementation of AI and ML. When it comes to utilizing AI in software testing, efficiency, speed of
execution and greater accuracy is where AI stands out. In this article, you will get to know how AI and ML are going
to impact the future of software testing.
How AI & ML
Are Going To
Impact the Future of
Software Testing
Following are the five factors that will justify how Artificial
Intelligence (AI) and Machine Learning (ML) will constructively
impact the software testing domain:
1. Improved reliability and accuracy: There will be scenarios where QA engineers will not be able to detect certain
bugs. From a QA engineer’s perspective, it is generally accepted, as manual testing may contain a certain amount of
errors/defects. But, the brand’s image can be at stake, if a consumer notices these bugs/defects/errors before
project stakeholders do. To solve this issue, Machine Learning (ML) can be bought into use. The source analysis can
be grasped by training the systems so that the acquired knowledge can be applied in the future.
More accurate results can be yielded by using this AI testing technology. Apart from that, the probability of human
errors is eradicated, test run time is reduced and every possible defect can be found. QA team need not bother about
large amounts of data. More reliable tests can be produced through the AI and ML platforms.
2. Time to market is streamlined: Manual testing is considered to be a time-consuming process and upon that
maintaining a speedy test process is considered to be a daunting task. Time-consuming and repetitive tasks can be
facilitated by integrating AI/ML platforms and thus automating them. Developers are benefitted, as investigating
large data sets and writing test scripts can be managed quickly and effectively.
3. Enhanced test coverage: The overall depth and scope of QA tests are improved through the platform of AI, which
in turn leads to a considerable improvement in the software quality. Proper access to data sets, file content, memory
etc., is obtained by test automation so that the software’s working status is ascertained to know whether it is
working as intended or not. Over a thousand test cases related to each test case that offers coverage can be easily
executed by implementing AI testing.
If there is a change that has been made in a certain section of the code, then in order to test those changes, a
minimum number of tests can precisely be identified by the right AI/ML tools. The current test coverage can be
properly inspected by these tools and thus specific areas can be pointed out that are at risk or have less coverage.
4. Visual validation is automated: Image-based testing is one of the popular testing methods used by the AI/ML
platform. It uses automated visual validation tools. This testing method is performed to ensure that the UI appears
to the user in a correct manner. Through visual testing, users can be certain of the UI attributes such as the right
shape, color, position, size etc.
AI/ML-based visual validation tools can be leveraged to perform testing in an optimal manner. A simple visual bug
in the application can be automatically detected by performing a simple ML test. The visual correctness is also
validated.
5. The sheer value of automating test cases: Spidering technique is used by the Machine Learning platform to write
automated test cases. The specific AI/ML tool needs to be pointed out to the application and the crawling process
will begin automatically. Some useful data such as the page load time, HTML of the page and screenshots are
obtained as the tool crawls. The cycle is continuously repeated until enough amounts of data are captured so that
the ML models get trained in order to comprehend the patterns in the application.
Improved
reliability and
accuracy
Time to
market is
streamlined
Enhanced
test
coverage
Visual
validation
is automated
The sheer value
of automating
test cases
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