How AI and Machine Learning Impact Software QA

Timothy Joseph
Timothy Joseph | July 7, 2020

How AI and Machine Learning Impact Software QA

Every company wants the future of technology implemented today. That’s why the AI industry rapidly grows year after year and continues to be one of the biggest automation testing technologies trends in software testing.

Adding machine learning and AI to your QA testing strategy can be both exciting and scary. It’s always fun to engage with new technology and discover all the key benefits of AI in QA test automation. But what impact does AI and ML bring to software QA? And should that change be embraced or rejected?

Are AI & Machine Learning the Same Thing?

We often hear artificial intelligence and machine learning used interchangeably in conversation. While being similar in nature, AI and machine learning are not the same thing.

  • Artificial intelligence is the ability for machines or programs to carry out tasks in a “smart” way. More specifically, AI is demonstrated when a program or machine responds with human-like behaviors to real world scenarios through its application of contemplation, judgement and intention.

    In short, AI programs or machines think and act humanly and rationally.

  • Machine learning is the process of teaching a machine or computer system how to make accurate predictions—or rather, make “smart” decisions—upon receiving data. Machine learning is a branch of AI based on the concept that machines can identify patterns, learn from past experiences and make decisions with little to no human intervention.

To say that AI and machine learning are the same thing is to say that about a banana and fruit. While a banana is a type of fruit, a banana is not all types of fruit. An apple, a peach and a pear (to name a few) are also fruit, but not a banana.

Same goes for machine learning and AI. Machine learning is a subset of artificial intelligence, while AI also encompasses other branches including neural networks, robotics, expert systems, fuzzy logic and natural language processing.

How AI Impacts QA Testing

How is AI going to change QA? Many professionals would argue that it already has. Successful QA teams can already give credit to AI for impacting their QA testing processes, from delivering faster, clearer results to creating easier test cycles.

But how does AI for QA testing impact QA testers? As companies rush AI into their testing practices, does that rush QA testers out of a job?

Don’t shift careers just yet. The value that AI brings to the QA testing process is because of its interaction with humans. AI is already impacting companies by enhancing the skills of QA testers and providing instant value to business growth.

Amplified Accuracy

Software testing will always require human QA testers, be it for data analysis or for exploratory and regression testing. Yet, we know all too well that even the most skilled QA engineers can make mistakes. The handling of extensive data can be overwhelming to testers, leading to lost focus on software QA and not catching all defects during the testing process.

That’s not the case for development cycles that include AI QA. In fact, testers that apply AI for QA testing receive more accurate results. This is because QA teams execute test cases with AI technology designed to acquire the understanding of source analysis techniques and revisit this knowledge in future instances. Using AI technology for data analysis significantly reduces—if not eliminates—human error while cutting down the time it takes to perform tests and locate defects.

Ascending Authority for QA Engineers

Artificial intelligence promises to provide greater efficiency during the entire testing process. But, with it increasing accuracy and decreasing testing time, where does that leave QA testers?

No need for your QA team to feel threatened. With AI taking on the responsibility of executing a variety of test cases, this opens up possibilities for QA testers to acquire new skills and sharpen current competencies. QA engineers who work alongside artificial intelligence will see a boost in their understanding of algorithmic analysis, neuro-linguistic programming and business intelligence.

QA engineers may find their roles quickly changing—and for the better. Companies that invest in AI must also invest in their employees to oversee this technology, transforming their position at the business from QA tester to:

  • AI Test Expert: Builds machine learning algorithms, applies understanding of math models and focuses on natural language processing paradigms.
  • AI QA Strategist: Applies understanding of math optimization, robotics techniques and data flow in discovering ways to implement AI QA practices throughout the business lifecycle.
  • Data Scientist: Filters data, reviews statistics and conducts predictive analysis for building essential models within the AI QA strategy.

Predict the Future with Precision

As more and more companies integrate AI for QA testing, we can expect to see improvements within customer processes. Fortunately for the IT industry, consumer demands rarely stall. However, consumer demands can always shift and choose the competition, meaning lower brand loyalty, reduced production and less revenue.

The solution? A company must collect as much data as possible in order to predict what their consumers want and launch that solution before competing businesses.

AI QA provides insight on future demands more easily through predictive analytics. With the help of AI technology, QA testers conduct data analysis in order to gain insight into consumer purchasing patterns.

How Machine Learning Impacts QA

Machine learning has come a long way since its early days on the AI scene. Not so long ago, machines relied on developers to continuously feed it a combination of algorithms, formulas, patterns and trends in order to produce results. But this isn’t “smart” behavior, not when the machines didn’t analyze data or learn from past experience.

This is no longer the case in machine learning testing. Programmed algorithms are still the foundation of machine learning in software testing, but now machines can evolve based on what they’ve learned through previous data interactions. 

Today, software testers find value in machine learning’s ability to identify predictive patterns within the data. One of the advantages of automated QA testing is that a variety of test cases, particularly back-end processes, can apply test automation in order to expedite the software testing process.

Here’s how machine learning can be used in software testing.

User Interface (UI) Testing

Machine learning in software testing can now be applied to UI analysis, something previously reserved for human testers. Many digital elements remain constant in design and functionality across companies and industries, such as how to filter search results, locate an online shopping cart and submit payments digitally. Because of this, machine learning testing can execute test cases analyzing the looks and behaviors of these elements. Through validation tools, machine learning also can carry out image-based testing to identify visual defects within the software, something almost impossible to pick up through human-led regression testing.

Strategy Orientation

Without machine learning in software testing, minor complications within the code often leads to lengthy tests for QA engineers to perform. Machine learning testing tools can determine the lowest amount of tests required to execute code modifications. Machine learning is designed to provide fast interpretation of data so that QA teams can identify current test coverage within the project as well as vulnerable areas within the software product.

Application Programming Interfaces (APIs)

Insight into APIs is difficult without the assistance of machine learning in software testing. Machine learning testing tools offer QA testers a way to carry out check-ups within the API layers. Instead of sending out a multitude of API calls, machine learning can quickly analyze all test scripts through its algorithms.

How AI & Machine Learning Can Impact Your QA Testing Today

The future of AI testing is now. Businesses that commit to implementing artificial intelligence and machine learning in software testing today can exceed consumer expectations of tomorrow. Your future success depends on the strength of your technology, so this is your opportunity to be a step ahead of your competition by applying the future of technology to today’s testing practices.

Upgrading your QA processes doesn’t have to be challenging or intimidating. Companies can partner with a reliable QA services provider like QASource to streamline the process. Our team of testing experts showcase years of experience in AI testing and can help you implement AI QA testing tools and machine learning testing practices within your development cycle.


This publication is for informational purposes only, and nothing contained in it should be considered legal advice. We expressly disclaim any warranty or responsibility for damages arising out of this information and encourage you to consult with legal counsel regarding your specific needs. We do not undertake any duty to update previously posted materials.