Maximizing Software Quality with Artificial Intelligence


Testing solutions that use artificial intelligence help development teams more easily analyze and understand where and what to fix and gives them the ability to more easily analyze and aggregate terabytes of data generated from automated tests.

It is no secret that the pandemic has fueled a permanent shift to customer-centric, digital-first experiences, making it essential to provide flawless applications. As such, the field of QA and software testing has become central to building successful development organizations. Innovations such as artificial intelligence (AI) and machine learning solutions that uplevel and automate a number of testing scenarios are becoming necessary to keep up with the growing demand for continuous testing. This includes helping teams to prioritize testing more effectively and only testing new features or pages that are being widely utilized by customers (to save time!).

Gartner has routinely listed artificial intelligence and machine learning among their top emerging skills for application development. But considering that an estimated 85% of AI projects fail to deliver on their goals, it’s clear that many software development organizations are struggling to understand what skills actually help their teams harness the power of intelligent technologies.

Emerging AI and Machine Learning Technologies for Software Testing

Today AI and ML are helping quality teams by ensuring that tests are only run when the application reaches the correct state, making sure that developers and testers can dedicate more time to fixing defects rather than investigating accidental failures. Using intelligent testing solutions gives the team the ability to more easily analyze and understand where and what to fix as well as the ability to more easily analyze and aggregate terabytes of data generated from automated tests into dashboards – especially as new features are added. These advanced reporting features help QA teams efficiently identify small changes or errors –and ensure that anomalies are addressed before they lead to more severe issues.

See also: Faster Software Development Should Not Equal Loss of Quality

Using AI to Optimize Test Coverage

Test coverage is a core measure of success for software testing, especially for development leaders looking to understand if their existing testing strategy is effectively measuring application quality. But while test automation has made software testing faster and more efficient, quality teams are still struggling to prioritize end-to-end testing and optimize test coverage as the product evolves.

The issue becomes even more complex when new application features are added since there’s no easy way to determine where more end-to-end tests are necessary. The result is often a testing strategy that becomes less efficient as the product evolves, slowing the development of new features.

Today, machine learning can combine similar application URLs to give testing teams useful insights about real application usage, allowing quality teams to tie test coverage directly to customer satisfaction. This means that rather than test every page, teams can prioritize testing far more effectively by focusing on the most commonly used functions. This provides an invaluable means to prioritize tests that reflect how customers are actually engaging with the website or application.

When software teams don’t account for how users are interacting with the application, quality professionals risk testing customer journeys that aren’t relevant, rendering testing less efficient and potentially slowing down development cycles.

The upshot, an ML algorithm can help test the algorithm’s performance against multiple sets of real-world web application data and prove its ability to recommend pages that need testing.

When testing is more efficient, testing teams are more proactive and adaptable, and test coverage becomes more meaningful across the development pipeline.

Using AI to Discover Small Changes Early in the Process

There are often small issues in the test data that may not grab one’s attention at first but could eventually result in serious issues. For instance, let’s take upgrades – these can cause a website to slow down incrementally. Every time a change is made to a website, it could end up taking 10 extra milliseconds to load. One might not notice such a change at first, but it can easily escalate over time, with the application becoming much slower, frustrating users and leading to higher customer attrition.

Another example: Say a software company has a hundred thousand customers and expects an average of 10 errors per day. That wouldn’t be very noticeable at first.But all of a sudden, they see a jump to 20 errors, which might provoke a customer to reach out for help through the in-app chat. This seemingly small change doubles the number of errors and could be detected as an anomaly by an AI testing solution. ML can help to identify these issues earlier – before they affect customers and identify what is causing the slow-down. When issues are detected earlier, teams can more easily address them before customers experience problems.

The Key to Utilizing AI/ML is to Understand “Good” Data

Part of quality engineering is preparing QA to take on a wider-ranging role in ensuring a positive user experience and accelerating product velocity. ML predictions are only as good as the data used to train them, making it important for QA teams embracing AI to understand what good data means. As teams build their knowledge, quality engineers should utilize data science to audit their existing software testing strategies for optimal data outputs, making their practices more efficient and easing the transition to automated testing.

Today, AI/ML is playing a large part in the innovation of modern software testing solutions – helping DevOps teams harness large data sets and infusing both speed and quality into the development pipeline.Real-world data enables QA teams to optimize testing for speed and quality, enabling testing at the speed of DevOps.

But as important as artificial intelligence and machine learning are to the future of software development and quality engineering, most QA professionals are too busy to become AI experts. To maximize their time, effort, and skillset, QA teams are better served by mastering key artificial intelligence and machine learning fundamentals that will enable them to start embracing advanced testing techniques and AI-based solutions as quickly as possible.

Lauren Clayberg

About Lauren Clayberg

Lauren Clayberg is a recent graduate of MIT and a software engineer at mabl. She recently completed her M.Eng. in Electrical Engineering and Computer Science, concentrating in Machine Learning, and an S.B. In Computer Science and Engineering, with a minor in Mathematics. She is very passionate about solving real problems. “I would consider myself to be an algorithm enthusiast, regex connoisseur, theory of computation nerd, and a musician on the side. Teamwork has been the driving force behind some of my favorite projects.”

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