Why has AI become so important in Software Testing?
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| Software Testing |
The increased emphasis on the quality of software
and accelerated time to market has made software development and testing a
complex affair. Add to this the complexities arising out of API interaction and
leveraging of legacy systems and you end up mulling about a new and innovative
way to manage the whole canvas of software testing. Moreover, studies have
shown that the latest software testing approach involving a greater testing of
mobile and hybrid applications has shrunk the actual time for development.
The breakneck speed of the launching of new
products or services has reduced the scope for keeping the development team
updated of the impending software issues. This in turn has made the developers
and testers understand and appreciate the need for a smarter and efficient softwaretesting approach. Since artificial intelligence and machine learning have
become the new technology juggernauts, it is about time the software testing
companies also adopt these to bring efficiency, scale, and speed to their
testing efforts.
Before extolling the virtues of artificial
intelligence and machine learning, let us first understand why the traditional software
testing methodology has come a cropper in meeting these challenges.
Changing user preferences: The traditional software testing approach is more about
identifying and fixing the mismatch in a test case when an input does not
behave as per expectations. In such a case, the tester should typically run a
checklist to mimic the steps of the users. Such an approach used to suffice
when the amount of data was relatively manageable. However, with the
development of new applications having multiple interfaces with devices,
platforms, networks, and frameworks, the amount of data generated has become
humongous. Consequently, the testing of such big data with the traditional
method of QA has become difficult and calls for a software testing approach
involving new technologies such as artificial intelligence, machine learning,
and predictive analysis.
Shortcomings of manual
testing: Humans are not capable of showing constant
attention to the job at hand. They are prone to making errors and overlooking
the bugs in the system. Besides, any repetitive nature of testing would leave
the human element exhausted and error prone. This is where test automation with
machine fed intelligence can produce accurate test results. So, no matter how
big the data is or how frequent or repetitive the tests are, AI and predictive
analysis can be of help.
Greater automation: The manual way of testing has its glaring restrictions. For
example, humans cannot work round the clock and have a limited attention span.
On the other hand, machine aided testing can be done on a consistent basis
irrespective of the time period. This approach suits the requirements of Agile
and DevOps especially related to continuous testing, integration, and delivery.
Thus, not only the testing gets accurate and time saving, it enhances the user
experience and ROI as well.
Let us find out how software
testing with AI can be made possible
Predict customer needs with
machine learning & predictive analytics: The
changing market dynamics and increased competition have necessitated the need
to predict what the customers want. So, instead of the customers reaching you,
it would be better if you reach the customers beforehand with their choicest
products/services. This would mean predicting their shopping needs by analyzing
past consumer behaviour. The behaviour would bring to light the shopping
patterns and give you an insight into the kind of products or features the
customers would seek.
Streamline the testing
process: Frequent product releases and updates can put a lot
of strain on the testing team and processes. The machine induced testing a la
AI can register changed controls and updates much more efficiently than humans.
Importantly, the vast range of dependencies with hundreds of input combinations
can be validated accurately and consistently with AI.
Types of tests and data driven
decisions: While planning for QA, testers
are often at their wit’s end about finalizing the test processes. This is where
AI with its user behaviour analysis can be of help. In fact, AI can ascribe a
risk preference while analyzing the user behaviour. It can monitor, evaluate,
and categorize the same, and determine the kind of tests to be conducted.
Testers, on their part, can automate redundant test cases and take data driven
decisions. When time is a critical factor in testing, it is better to focus on
crucial test cases and give them a greater coverage by leveraging risk-based
automation.
Saving time and cost: When the help of AI is taken in terms of creating, modifying,
analyzing, and executing test cases, testers can do away with manually updating
them. They can rather identify controls and defects much more effectively.
Also, when the source code undergoes modification due to repeated software
testing, validating them each time manually is time consuming and costly.
Whereas an AI based test automation can be executed repeatedly at a faster rate
leading to cost savings.
Use of AI based tools: AI or machine learning based test automation tools such as
Functionize, Test.ai, Testim.io, Appvance and others can create, execute, and
maintain various types of testing thereby increasing the stability of test
suites.
Conclusion
Artificial Intelligence and machine learning have
found their way into the SDLC leading to a better authoring and executing of
test cases. Notwithstanding the initial investment in implementing an AI based
test automation system, the results in terms of better products that suit the
customer needs, can save costs and improve your brand equity.
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