Today’s applications don’t stop at just one level. They encompass multiple aspects and facets of a user’s interaction, indulging them with APIs, leveraging legacy systems, accessing databases, and delivering push notifications to users, all at once. Clearly, to make something like that happen, a lot of hard work and trial-error processes have to be at play, don’t they?
This is where software testing comes into the mix. The challenges to testing applications and software have never been as large or daunting as they are today. Throw in quality assurance and control to the mix, and you have yourself a mountain that seems harder to climb than the mighty Everest.
Thankfully, AI is here to save us from the blues. Some actually believe that AI will magically change the way we test and process quality assurance and control. The key lies in leveraging artificial intelligence to verify the ever-increasing, gargantuan amounts of code that we have on our hands. Can we do it? Is it possible to learn this power? Let’s explore.
Some more reasons as to why we need AI in software testing:
Enterprises, giants of the tech world, or even food delivery agents- all want applications and software that is agile, that can outperform the competition, and that is easy to use with an eye-candy UI. The result- the number of lines of code that developers and designers have to type in goes through the sky. Millions and millions of lines of code go in towards making a simple interactive application, and assuring zero errors across these million lines is a task in itself.
The onus lies on software testers who have to make sure that everything is spick and span, before releasing the app to the client or the market. We know how code is like- one semicolon here or there, and you’re going to be spending your days and nights wondering where it all went wrong, how did you get into this, what’s it going to take to fix all this mess.
Clearly, software testers and developers need a little bit of assistance to save their souls and sanity. Worry not, here comes the superhero we call artificial intelligence! It promises to ease some of the major problems that testers have, including identification of use cases, verifying app behavior based on input data, and checking the quality of the software or app on multiple parameters. Thank you, AI!
The pros of incorporating AI in software testing:
Let us now take an in-depth look at where AI offers a better solution than what is currently available when software testing is considered.
The first advantage of AI in software testing is the most obvious one, saving time and money. In addition to apps having millions of lines of code, these lines are modified and updated almost daily. Any changes to code mean testing those changes, and manually repeating such tests can be both times consuming, and costly.
Automated tests to the rescue! One of the most basic utilizations of AI- automation- can be used to reduce the timespan of repetitive software testing, and incur zero additional costs.
AI can even help achieve results that are beyond human capability. Take an example- if you wish to conduct a controlled app or software test with thousands, or even hundreds of users, will human capabilities be able to handle it? Probably not.
Another aspect where humans can lack in testing is accuracy. With so much code to analyze and run, errors are bound to go undetected. Even the most careful of testers might slip up once in a while, and a small design error in the software goes past their eyes.
How does AI help in these cases? One- it makes it incredibly effective and easy to automate tests with thousands of users. These “virtual” users interact with the database, network, and software, which is necessary to carry out a thorough test.
Two- AI won’t skip over mistakes as humans might. Computers running automated tests will analyze each line of code carefully, without missing out on recording any errors- that’s how computers work, don’t they? The human capability of testers can now be put to creative problem solving or dealing with the testing of sophisticated features. Also, read 4 Ways Robotics Will Transform the Manufacturing Industry.
Defect analytics:
Another area where AI provides a great use case. Defect analysis is what makes sure that all bugs and errors are caught at the root, and are not errors arising to an earlier defect that has been left unattended. AI and ML-based algorithms can learn from past trends and speed up the standard defect analytics processes. The amalgamation of machine learning and AI can create pretty nifty algorithms that help firms employ defect analytics quickly, and with the least amount of additional costs.
Coming to mobile software testing, here’s another area where AI presents an advantage over regular testing- API testing and maintenance. Application Program Interfaces, or APIs, are what power mobile apps. For each tap that you make on your screen, the application interacts with a different API in one form or the other. Obviously, this makes it very essential and equally difficult to carry out human testing of all API combinations, and maintenance of these interfaces.
AI here understands the entire process that goes on in and around the API resources. Manual UI tests can be automated for all different scenarios, including test cases in the library of the user. What’s more is that AI can even help create more advanced tests, in addition to automating the existing ones. Automated API testing facilities that learn and update themselves- sounds like a dream!
Cons of AI-based Software Testing:
While AI presents an incredible amount of promise in software testing, there are some basic character flaws that we must face, regarding current software testing efforts that involve AI. How they evolve over time will be interesting to see.
Needing human guidance to provide insights:-
Those hoping for AI to completely replace humans in the software testing domain- look away! AI is indeed going to replace the labor part of manual testing that we so despise. But fully automated and intelligent testing is still a distance away, although we have started progress on those with API testing facilities. What we still need to do is provide human input to the AI- monitoring, inputting, and fine-tuning the behavior of our artificially intelligent friends every step of the way.
No empathy:-
What computers, AI applications, and robots are known for, is that they don’t have feelings. Sometimes, that can be a problem when it comes to software testing. Your app might run fine, it might do exactly what you want it to do, but it may happen that the app takes forever to load up. Human testers would have an emotional response towards this slow loading time, which obviously would be an inconvenience to users. AI testing, however, would completely skip this part, and do its fair share of waiting until the app loads. The AI doesn’t need to catch a train, it can sit there and wait all day! Your users can’t. This is where we believe AI is still flawed when it comes to software testing in UI and design.
To conclude:
Powerful tools with AI that are used to automate and speed up software testing are being used by companies all over the world. Sure, human intervention is required, because these tools aren’t capable enough yet to learn by themselves and evolve over time. Humans, you cannot escape the world of software testing completely (as of now, at least)!
AI in software testing sure offers plenty of pros than it has cons, and for now, a perfect base of human capability, sprinkled in with a flavor of AI- this is what software testing recipes need. As for the future? Stay tuned to find out what happens. It is going to be very interesting, to say the least.
Author Bio: James Grills is a technical writer with a passion for writing on emerging technologies in the areas of mobile application development and IoT technology. He is a marketing advisor – currently associated with Cumulations Technologies, one of the best app development company in Bangalore.
