Digital Transformation is making a huge
business impact. This is mainly driven by the advent of new technologies,
development/adoption of new products or business models, and growing customer
expectations. The increasingly competitive nature of online business has led
enterprises to aim at garnering more customers to realize the ROI.
As the growing competition has resulted in
new product launches, the need to validate their quality through software quality assurance has become
important. Faced with such constraints including that of time and cost,
enterprises look to leverage Artificial Intelligence and machine learning to understand
the needs of customers based on their past behavioural patterns.
Moreover, the spread of Internet of Things
(IoT) and its growing scope have increased the need for testing the IoT
processes, embedded devices and the software within them. For in the absence of
any software quality assurance
vis-a-vis IoT, the devices would falter in their functioning leading to
unpredictable outcomes. However, the use of traditional methods for testing
embedded devices can be time consuming leading to a bad user experience. This
is where AI and machine learning can help in predicting the user traffic,
delivering real time insights and bring about a drastic improvement in user
experience.
As enterprises deal with big data, they
look to leverage the same to deliver productive outcomes. The use of data
mining and predictive analytics a la AI can help businesses to optimize their
processes, predict customer behaviour and create better products. One of the
foremost challenges of Agile and DevOps based test automation is identifying
processes for software qualityassurance testing. This is due to the fact that not all processes
can be automated owing to process complexity, lack of time and resources to
write the script, and cost. AI and machine learning can help in identifying
processes to be automated based on repetitive patterns of data. The use of AI
and machine learning can help improve the test efficiencies and enable better
decision making.
AI can optimize
software quality assurance in the following ways:-
Analysing the
defects: One of the main objectives of Agile and DevOps based testing is
identifying and eliminating glitches early in the design and development phase.
AI can help in identifying the most critical glitches as opposed to the lesser
ones. Thus, the QA software testing
team can prioritize regression testing leading to quicker turnaround times.
Analysing Customer
behavioural patterns: The testers can leverage the use of
monitors or embedded sensors to generate behavioural patterns. These patterns
throw light on the demographics, geographies, and devices of users. The same
can be used by testers to build better test suites for achieving a greater test
efficiency.
Use of social
media: Effective test suits can be built by analyzing suitable data
patterns from social media usage of customers. This is in terms of identifying
the demographic trends.
Enhancing QA
testing: AI can help in optimizing test cases, prioritizing testing, and
reducing the task of analyzing complex data patterns. Moreover, by mining the
test management data from dashboard, the productivity of testers can be
ascertained. The productivity is related to the creation and execution of test
cases. The knowledge can be used to bring efficiency in the testing process
leading to faster test outcomes.
Non functional
analysis: AI can help in generating performance
reports, identifying security vulnerabilities and SLA misses based on the
operational dashboard. The information can be used to plug security
vulnerabilities, reduce performance issues and meet SLA targets.
Better feedback: QA
testers can get a better and quicker feedback through AI. The analytics element
in AI can run test cases quickly by mimicking the input test scenarios. These
scenarios can be very tedious given their repetitive and complex nature. AI can
generate a suitable output from such scenarios based on past data patterns.
Predictive
analytics: Test automation leads to the generation of
vast amounts of data (read big data.) The big data can form the basis of
identifying and forecasting the quality of processes and products. As a
consequence of identifying the test outcomes or inherent glitches in the
system, AI can guide the tester by prescribing a better course of action.
Conclusion
As the demand for quality products with
quick turnaround times increases, AI and machine learning can be of help with
their data based analytics. AI helps in optimizing QA software testing a great deal and leads to the development of
better products.
This Article is originally published at Medium.com, How can AI optimize Software Quality Assurance?
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