What QA Teams Should Know About Machine Learning For Software Testing

 Talk to any industry insider, and they’ll tell you that the landscape of software testing is undergoing a paradigm shift that’s rendering many existing practices inadequate. The pace of software delivery is unrecognizable from only a few years ago as tech companies release products at breakneck speed, driving quality assurance (QA) teams to expand their toolkit in order to remain competitive.

Cognitive technologies that simulate activities of the human brain, such as machine learning, have stepped up the rhythm of testing and product releases in a major way. This has led to novel software testing approaches that deliver more refined applications with error-free functions delivered in less time and without programming.

In this article, let’s explore how machine learning is revolutionizing software testing and breaking new ground for QA teams and enterprises alike, as well as how to successfully implement it.

1. Machine learning can result in less software test maintenance.

Whenever developers implement new application features, QAs must run maintenance testing to check if the changes have compromised existing functionalities and that the application continues to run as intended. But the tests themselves also have to be repaired to ensure they don’t fall behind code changes.

With machine learning, QA testers gain a sharper edge because they’re able to home in on what’s really important and increase the quality of the application. In other words, they can:

• Automate the testing process.

• Prioritize bugs more efficiently.

• Deliver superior results with less staff.

• Reduce the risk of overlooked bugs.

• Predict occurrences and changes that can lead to more defects.

2. Computer vision can simplify test analysis.

The World Quality Report has pointed out that companies are quickly improving the application of computer vision technologies. Computer vision tools, which enable operating systems to derive sophisticated knowledge using digital images and videos, are gaining ground in the space of QA due to their unique ability to perceive tasks in the same way that a human eye can—and then automating them. Doing so helps machines adapt themselves in an environment enabling them to conduct repetitive detection efforts and test more of the user interface (UI) than traditional testing tools.

Specifically, computer vision can use image checks to figure out if image components are related to one another and then identify the image itself. As a result, it can deduce whether these images can integrate with thought processes and if they evoke a desired feeling or elicit appropriate action.

3. Machine learning can make test creation quicker.

Machine learning tools can help QA testers generate test data, research data suitability, optimize and analyze the coverage, and perform test management with greater efficiency than in previous years. As examined in the academic journal International Journal of Artificial Intelligence and Applications, today’s machine learning tools not only simplify test creation, reduce gaps in testing coverage and repair tests to align with new requirements, they analyze changes in application to:

• Generate test cases more intelligently. With machine learning, QA testers can automate the generation of test cases to regenerate the tests each time the application changes, build automated oracles that model the behavior of the user interface and generate tests based on AI planning techniques and genetic modeling.

• Improve test cases and boost test coverage with greater efficiency.

• Ensure requirements coverage.

What is the best strategy for machine learning implementation?

When implementing machine learning technologies, first come up with a game plan and set expectations within your team. This means determining data requirements and assigning roles and responsibilities. At the same time, set up a change management process and routine assessment of outcomes as part of monitoring and evaluation.

Next, determine where machine learning makes the most sense—which cracks it can fill, what processes it can help streamline and where it will expend the least amount of resources, such as time, staff, etc.

Finally, apply machine learning only toward these areas. This will help you keep a lid on your budget and evaluate whether the expectations you set for yourself have been reasonable. For example, if you have implemented machine learning for test case prioritization, have you reduced application malfunction and increased software stability?

Remember that there’s no substitute for human testers.

A common problem for QA leaders is to assume that machine learning can replace all manual testing. This can overwhelm the system with too much data and diminish its performance. Instead, think of AI technologies as an enhancement to your existing QA operations.

A machine that matches the sophistication of its human counterpart in reviewing and validating a software product is yet to be developed. This is why machine learning is best implemented toward repetitive tasks, time-intensive tests, tests that cannot be conducted manually and those with multiple data sets.

In other words, machines should be doing the heavy lifting so that testers can redirect their attention to higher-value activities, such as uncovering user motivation and checking if the application works as intended. QA leaders should also allocate time to gathering quality data, improving infrastructure, establishing reliable implementation processes and developing training programs.

Machine learning remains an untapped opportunity.

Increasingly shorter deliverable timelines and the need for faster and more competitively priced software products are compelling QA teams to continuously reevaluate if they’re using the right tools and if the processes they’re implementing are justified.

You would imagine that given these constraints, companies would be adopting machine learning tools with greater enthusiasm. And yet, according to the World Quality Report, there are few signs of significant general progress due to a shortage of relevant skill sets and the pandemic, which has disrupted schedules, budgets and plans.

While this is an industrywide problem, it’s also an incredible opportunity for the companies willing to embrace change.

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Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/02/02/what-qa-teams-should-know-about-machine-learning-for-software-testing/?sh=10d5e4524396