Software testing is a costly and lengthy process. Learn how as software becomes more complex the rise of AI and Machine Learning can simplify the process.


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The human brain is the greatest, most complex machine that the world has ever produced. Consider, just as you’re reading this article, everything that is unconsciously done simultaneously.

Everything within your vision has to be processed within microseconds of it being received, and most importantly is the deciphering of language, a man-made creation. From processing that information you may then immediately draw your own conclusions, envision concepts, theories, devoting new information to be stored in memory.

The mental gymnastics alone are only one part of your brain’s responsibility. What other minor occurrences are happening as well? Your breathing, heartbeat, typing, clicking, shifting, and any other minutiae of body movements are a product of an organic supercomputer. Nature has yet to produce another its equal. If nature is unable to replicate the human brain, then in a way it’s natural for us to do that ourselves.

Artificial intelligence still has a long way to go before it reaches anything resembling the human equivalent of life, but we’re making progress. AI, and Machine Learning by extension, have become increasingly popular within the past decade as both a buzzword and tech phenomenon. As the technology continues to be developed, it gains versatility in its use, able to be adapted to more and more different facets of technology, with software testing being no stranger.

Software testing we’ll get into soon enough, but our first stop on this train ride remains at the AI station. We’ve only scratched the surface of what it does and how it can be applied in the software development sector.


What Is Artificial Intelligence?

The simulation of human intelligence in machines that are programmed to think like humans, learn from experience, and perform human-like tasks. AI is, in a cold, limited, and primitive form, a brain that excels in ways that our human brains don’t. For instance, multiply 126 by 89012. What may take you a minute, less with pen and paper, is done in the blink of an eye with a computer. By that same token, however, you wouldn’t leave a computer to accurately tackle existential issues, create new mathematics, sciences, art, or help a family member through financial struggle. For all the uses AI has, it’s still finite.

The basics of AI isn’t rooted in anything philosophical, moral, creative, or emotional, as we haven’t reached the point where it can use effective lateral thinking. Robots can’t think outside the box. However, AI are not only able to accrue an enormous amount of information, they’re also capable of taking that information into account to achieve a goal.

As you might imagine, there’s no single end-all-be-all AI program for your every need. Depending on what needs to be done, there’s several different AI types to choose from for the job. These different types of AI lean and implement knowledge in different ways by using different algorithms and programming. An AI intended to analyze facial recognition won’t be effective in organizing and maintaining a company’s employee attendance file, nor that for an AI designed for predictive text answers for your emails.

The types of AI, however, can be categorized at sufficiently high levels into two groups: narrow AI and general AI. Narrow AI are designed for specific tasks, such as any of the ones listed above, and are able to learn from their interactions with their work.

General AI, by comparison, are more science fiction than reality. The only true example equatable to what general AI can do are found in humans. Fictional AI, like HAL, Data, or Will Robinson’s robot, are capable of learning and accomplishing an enormous variety of tasks with little overlap between them. As of yet, we haven’t gotten to a point where those types of AI are possible.

AI are only capable of growing if they’re given information to draw from. Like people, a fresh AI may not be very useful, but once it’s been provided with enough data they’re capable of learning and implementing a variety of functions. An AI, like iTexico’s own HAL, may need to learn through trial and error. The more data is fed through the program, the more it will repeat the same tasks over and over again until it achieves optimal results.

AI technologies continues to grow into different facets, especially in areas you may not expect to need artificial intelligence. Expanding this technology will lead to remarkable developments in future trends of AI.


Traditional Software Testing

Now then, where were we? Right, software testing. 

Software testing is about locating defects in a piece of software by testing the state of a software and comparing it to what is expected. Varying approaches aside, the life cycle of the software testing process is generally structured and universal. After you’ve determined what needs analysis, you’ll then prepare and design the test. With planning settled, a testing environment is created, followed by the test’s execution. From there, it’s all results and completion.

The expectation that any piece of software is perfect on its first go around is a fallacy. Software design is notoriously prone to bugs, meaning you’re guaranteed to have a few quirks to work out in your system. These defects range from system crashes to unresponsive links. Some of the more common bugs you’ll come across include functional errors, missing commands, logical errors, communication problems, and compilation errors to name a few among many.

What’s relevant here aren’t the types of errors you’ll encounter, it’s the process you use to find those mistakes in the first place.


Manual Testing

Manual testing is the hands on approach. That means it’s painstaking, but effective, so long as you have time to burn and patience to be worn. With manual testing, you’ll be going through software by hand to see if each function of the software works correctly. By the end of the process, you’ll have a fairly cohesive idea of what works and what doesn’t.

The key here is involving yourself in a little bit of objectivity. It’s easy to view your work from the eyes of a developer, since that’s what you are, but thoroughness isn’t achieved through a one-way mirror. That is to say, you may be creating it, but you won’t be the primary user.  Your user base has the tendency to find mistakes where you weren’t expecting them to be. Therefore, you’ll have to go through this process with the mindset of both a developer and a user to ensure it works effectively.

As you can imagine, this type of cherry picking work is going to take time, and anything done by human hands is vulnerable to human error. To help turn this around, we’d like to introduce the concept of Exploratory Testing to you. Like wandering into an unfamiliar jungle, Exploratory Testing is perfect for gauging software that is incomplete, have low requirements, or when you don’t have much time to stress test new apps. Exploratory Testing isn’t the end all of software testing, but it provides quick turnaround on pertinent issues and bugs.


Automation Testing

The prospect of cracking your knuckles and devoting yourself to hours of poring through software isn’t appealing to anyone. As an alternative to manual testing, automation testing helps save your poor wrists from carpal tunnel and long, monotonous working hours. Instead of having a person check every part of a piece of software, automation testing uses scripts and software tools to run the tests.

There’s an obvious appeal here. Plug in a machine, let them run the diagnostic, and reap the results. That’s great, but it’s also an imperfect solution. While these are consistent for checking faults in a program, since they run on a script, they can be limited to what they are testing. Software is largely incapable of making independent choices regarding how they complete their work, after all.

For example, a software that adds a new feature may pass through auto-testing regardless of the functionality as the auto-test won’t know to test the new function. Ensuring that your automation testing code can effectively check for every new variable is something that falls on the software engineer’s shoulders to keep track. Automation testing is excellent for improved scalability, but due to its limitations it can still miss full coverage.


How AI Improves Software Testing

The less people have to intervene to ensure that a piece of software is properly checked, calibrated, and tested means more time for human productivity. AI can help with that. With the inclusion of AI in software testing, it can help expedite the delivery process in a few different ways.

The most obvious application that AI provides is through the gathering and compiling of new information. In this instance, for the purpose of software testing an AI can gather and analyze data from users of an app. This in turn allows AI to not only recognize patterns but to create new connections that a human developer might be unable to form.

This effect compounds with the more information an AI ingests. The more an AI learns, the more complex the AI can function, allowing it to more easily, quickly, and effectively analyze software for defects and malfunctions. We haven’t even mentioned how much quicker all this can be done when relying on the processing power AI offers.



AI Enhancing Manual Testing

AI may not be able to completely replace the human touch yet, but it’s sure capable of handling a lot of jobs we’d much rather not be doing. For instance, the long hours spent doing manual testing can be completed by AI at a much higher rate of speed.

With the inclusion of AI in the process, a task that could take days or even weeks by hand can be greatly expedited with a bit of clever software. This in total reduces time spent on manual testing, freeing you up from an awful slog of tedious work, while also reducing development costs.

Included with faster speed of completion is the added redundancy for mistakes. You’re being thorough, certainly, but even you are liable to miss a typo or too. An AI is less likely to miss bugs than a human is by learning from patterns it’s analyzed while being able to be more thorough in the long run.


Improved Automation Testing

Just like it does with manual testing, including AI in your automation testing process is, in general, a good idea. AI can already do most of the same things as automation testing software can. They cover the same bases, but AI are also capable of doing more than automation testing alone can.

The ability to learn is what puts AI above the call of duty here. As a defining feature of an entire sector of technology, an Ai with the ability to learn is able to provide a fuller coverage of testing than automation software will. Automation testing, after all, is a start and stop process that you run from beginning to end. AI, on the other hand, will continuously explore software once it’s been assigned.


AI Evolving Software Testing

AI grows and adapts. Nothing too extreme, of course, it’s not Skynet after all. However, with what little learning capability we’ve designed into each program, AI technology is advancing faster than any technology before it.

Taking now, from the moment you’re reading this article, the only thing we truly know about the future of AI is that it’s limitless. There are unknowable future improvements to AI testing that may be here in years to come. It may very well evolve on its own to catch bugs that would have never been caught in the past by humans or machines alike.

As software grows and becomes more advanced, ensuring that new programs are up to par for the userbase’s expectations grows more demanding. The need for thorough testing that’s cost efficient and able to scale quickly is imperative. Since AI is the only capable technology of fitting that docket, it’s essentially the future of software testing as we know it.



Software testing is only one sector of the multitude of applications that AI can offer to the world. Jobs that have traditionally only been filled by human hands can be passed off to thinking machines, lightening our load. That is, after all, the ultimate goal of a machine-driven goal: a world that’s easier and better for the human race.

In traditional software testing, the work was either done completely by a person or by a simple automated code. Alone, either of these gets the job done, but not without tradeoffs that prove to be steep prices in their own rights. Manual testing means long hours put in, chewing up a person’s entire work day for days or weeks. Automation testing alleviates that burden a bit, but it can’t account for every available in the way that people can.

By including AI in the process, software testing becomes easier, more efficient, and more thorough. With manual testing, a process that could take weeks can be shortened to a percentage of that time. Automation testing, when paired with AI, is essentially swapping out an older version for an update, better model. AI can already do what automation testing does while adding more functionality on top.

iTexico is hard at work supplying the future of artificial intelligence to the world. If you’re interested in how iTexico can offer AI service for your company, check out our AI Innovation Center for more information.


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