Software Testing

Is AI Eating Up My QA Testing Job?

AI and QA Testing Job


A few months have left for 2017 to end and Artificial Intelligence (AI) has already checked in the Software testing industry across the globe. Within the few months of its release, the technology has taken everyone by storm. The current economy of UK has reached another tipping point. Due to which software organizations need to be especially smart with time and resources while still driving rapid innovation. This calls for adopting the most effective, flexible processes for development, QA, and testing. Putting everything else aside, here we will be simply focusing on AI in Software Testing- How good or worst it is?

The Concept of Artificial Intelligence

It is pretty much assumed that AI technique is about to dominate IT within the next two decades. Do you remember the “partnership on AI”, the joint initiative of world software giants like Amazon, Google, Facebook, IBM, Microsoft? Yeah! It’s just one of the many signs that serve to confirm this. Now when someone asks me to define the concept of AI, I usually split it into two different categories; the first category is to emulate single human competencies such as letter recognition or voice generation. This covers all of the current applications that can be referred as AI applications and the second category incorporates systems performing creative activities- spontaneously without any request. It is characterized by consciousness, feelings or self-awareness.

The first category is used in various scenarios where different techniques are successfully implemented across all aspects of our lives. Software testing is one of the areas where most of the human tester’s activities sooner or later will be automated.

Is It Going To Steal Your QA Testing Job?

The previous year at a major testing conference, around five executives sat against 300 testers and declared adamantly that machine learning, a branch of artificial intelligence, would take over software testing. They were right as well as wrong. Let me show you how? Machine learning and AI are basically technologies created to make any tasks easier, faster and more reliable. So, they won’t necessarily eliminate testing jobs but of course, it will change how the work gets done.

Within the span of 60 years, machine learning was the first envisioned technology applied in a wide array of fields. Right from how to identify cancerous tumors in kidneys, types of cancer to teach driverless cars where the edge of the road is, project trade securities in finance and potential losses due to natural disasters and so forth.

AI in Software Testing 

Appdiff can be considered as one of the strong pioneers in AI-assisted mobile testing. The CEO and founder of the company Jason Arbon say Appdiff built its AI-driven mobile testing platform to enable mobile application testing without any human involvement. Teaching a bot, how to do gestures with iPhone and Android phones, what actions could these agents take on different apps, how could it navigate through an app, were some of the most crucial challenges faced by the dynamic team at the Appdiff.

Now being a tester you know that interact with a mobile app isn’t what testing is. There’s much more that we do at any Software Testing and quality assurance company in UK. The requirements range from understanding the business domain to knowing a set of heuristics for exposing defects, knowing how to think like the best and worst users of the application, offering solutions in favor of company’s interest and those of users.

Jason predicts that “writing software is a field ML will conquer before it will conquer testing.”  

With continuous delivery, integration, and DevOps being the hot topics of every software development conversation these days, the testers might feel pressurized. It is simply because the crew cannot keep up with the amount of testing that should happen.

So what needs to be done?

Stay Ahead Of Times

It may quite interest you to know that Arbon, who previously worked at Google and has a background in software testing, realized a fundamental truth, i.e. Almost every app is the same. As a result, he created bots that specialized in each area of the app and were better than any average tester. Though they’re not as smart as a human might be, they’re the best search testers on the planet.

Like it or not, there is a silver lining for software testers in the UK and all across the globe. Jason says- though the folks we work with don’t get fired but they get to hand off work to do the human, creative things the ones at which they are already better. “We want people to be able to do complicated edge cases, not the routine stuff. This is to augment testers, not replace them.” 

Apart from this, here I would like to mention few areas where QA teams would benefit from leveraging artificial intelligence in software testing:

  • Behavioral patterns in application testing- Its mechanism to use monitors. For instance, have you ever come across those IoT sensors? They are used to generate behavioral patterns by geography, devices, and demographics as inputs to build test suites.
  • Social media analytics- It’s mechanism to analyze data from social media in order to build trends by demographics; forming the basis of test suites.
  • Defect analysis- This is a mechanism to mine defects and defines test suites. It basically assists a tester in making intelligent decisions about test coverage and test suite optimization at a rapid pace.
  • Estimation and efficiency analysis- Mine test management tool data for tester productivity such as test case creation and execution to automate the decision-making process on when to release and when to start testing.
  • Nonfunctional analytics- In order to form the basis of performance and security testing, one needs to analyze operational logs to determine performance SLA’s and security vulnerabilities.
  • Machine learning test programs- You can create a software test data generator and input a teaching set of flat files of data, some of which say, “this is a valid input”  based on contents of flat file sequences and some of which say, “this is not a valid input.” Then you could import feeds from production, and the test data generator could parse the inputs based on the rules that it has learnt to create valid test data. 

Author Bio:

Nishtha Singh works as a Presales Manager with at TatvaSoft UK. I relish writing about various technology trends, Digital Marketing,  Management, Entrepreneurship, Startups and much more. My aims to spread knowledge of the latest technologies through my online contribution.

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