Does this sound familiar? Your team creates software, tests it rigorously for bugs, and releases it in the market. And just when you are about to take a sigh of relief, BOOM, it's time to release the next update or feature. It’s time to go through the entire testing procedure all over again. It’s time for the same frustrating days that you thought were over. Well, at least for some time.
Software
testing can be a pain, and if you are a large enterprise with software that has
complicated architecture, it could easily become a reason for your sleepless
nights. We understand this because, as a software
testing company in the USA, we faced a similar problem when we were just
getting started. But for every problem, there is a solution.
For
this problem, the solution is Artificial Intelligence and Machine
Learning. And we can say that with confidence
because we have been using it for many years, and it has served us well.
In fact, we reduced the overall manual testing time by 80% on every release for a client with automation testing.
Challenges
Faced by the Software Testing Industry (and how AI can solve it)
1.
Tight Deadlines
In
today’s cut-throat world, if you don’t keep your customers engaged and
interested, you’ll lose them quickly to your competition. To avoid that, you
must release new features and updates quickly and keep your audience on their
toes for the next update. On top of that, the new feature must work flawlessly,
and the entire architecture of the software should be able to sustain the
weight of new users.
We
understand that’s a lot. Because that means you are constantly on a deadline to
deliver results. And you can make no mistake with testing. Also, there are so
many tools and testing suites to choose from, it could become a nightmare. That
is the most massive challenge that organizations are facing today.
How
AI Can Solve This
The
AI development services
make it easier for testers to understand which test cases they must execute in
each test cycle. The best thing about it is that it automatically gathers data
from previous application test executions, allowing it to easily detect changes
in the code. It also suggests test suites based on the application's risk,
which are defined by taking several factors into account.
That
includes (to name a few)
● Identifying
the most frequently used parts of the application
● Tests
with a high failure or complexity
● The
probability that a cyber-attack can occur.
This
method will help detect defects and bugs early on. Also, it will prove useful
in running test cases that might get affected at the start of a test cycle.
That will facilitate quick feedback from delivery teams and ensure that software testing service providers find
solutions when they discover any defects.
2.
Low Accuracy
As
technology gets more advanced, smartphone companies are able to reduce the
prices of mobile phones. The reduced cost and improved internet connectivity
have prompted more people to get smartphones. In fact, the number of active
smartphone subscriptions has crossed 6 billion
worldwide.
That
means people will access apps and websites simultaneously from thousands of
different combinations of platforms, devices, and browsers. Testing software
for such diverse environments can lead to low accuracy, which could be a
problem for your brand image.
Apart
from that, there are many repetitive tasks that testers have to perform
manually. That could also increase the chances of human error in QA testing.
How
AI Can Solve This
The
best software testing company will
employ automation to reduce the workload of testers by taking care of
repetitive tasks. When AI is used to power automation, it performs repetitive
tasks while testing teams get the freedom to complete more productive work. As
a result, it saves time and improves accuracy for achieving quality results.
3.
Inadequate Test Coverage
With
the use of virtual reality and augmented reality on the rise in applications,
the scope of testing also needs to increase accordingly. To be robust and
flawless, an application must be thoroughly tested in a variety of
environments. For complex applications, your testing team must ensure that
every line of code is subjected to rigorous testing. Therefore, this process
might take a long time.
However,
you don’t have time since your users expect you to deliver updates timely. That
puts undue pressure on the testing team. Due to this, QAs may fail to
prioritize key test cases, resulting in inadequate test coverage. This leads to
insufficient testing and allows bugs to enter production.
How
AI Can Solve This
Artificial
Intelligence technology is effective at
expanding the scope of test cases. It monitors the app's memory, internal
programs, etc., to ensure that the functionality is up to the mark.
Furthermore, AI-powered test automation tools can execute several test cases in
a single test run. This AI capability significantly speeds up and increases
test coverage.
4.
Script Execution Issue
With
complex apps and dynamic websites, the script execution could be an issue.
Longer load time, delayed response, and poor scalability are a few problems
that could arise. In the case of dynamic content, the locators that testers use
to identify the elements of a web page may fail to interact with them. That
will defeat the purpose of automated testing and will give poor results.
How
AI Can Solve This
When using the latest machine learning technologies, there is no need to automate
test scripts because AI will execute them automatically. It also makes test
cases more stable, ensuring there is no change when the locators are modified.
5.
Increased Cost
Due
to the above-mentioned problems, if a bug or glitch gets passed on to the final
and live product, it could become a financial nightmare for you. It would take
a lot of time and resources to identify, resolve, and verify the functionality
of the app. You may even have to hire additional testers to make your app
glitch-free again. That is just going to shoot your budget through the roof.
How
AI Can Solve This
As we discussed above, when bugs or errors in the application are not resolved in the beginning, they can become costly. And custom machine learning solutions help with it, which significantly reduces costs.
Furthermore, AI is even more efficient
at sorting files, detecting errors, and scanning code in less time. That
reduces manual labor and human error, giving more accurate results at a lower
cost. Also, you can hire a software
testing service team for the same.
Parting Thoughts
To offer the best experience to your customers, it is important to experiment with new technologies and lead innovation. But this is only possible if you test your solutions extensively, including every scenario and environment possible. As a leading software testing company, Narola Infotech can help you fulfill all your testing-related requirements.
For 17 years, we have tried and tested
different methods and strategies. And we have successfully applied those
solutions for multiple clients. Whatever your issue is, we are sure we must
have come across it before and have an answer ready for you. All you need to do
is contact us, and our expert will be with you right away!
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