The AI transformation is well underway within the software development space. With time and resources at such a premium, much of it devoted to repetitive, time-consuming tasks, the rise of AI should come as no surprise.
In many cases, it makes more sense to leave the search for defects to machine learning (ML), user feedback analysis to natural language processing (NLP), and proactive issue identification to predictive analytics. A survey of 1,000 professionals in the software field found that 65% already use AI in their software quality assurance processes.
Can you imagine improving developer productivity by 35–45% while reducing code development costs overall? It’s possible, at least according to recent estimates by McKinsey & Company. So when we say “transformative” to describe the role of AI in software quality assurance, we’re not overstating the issue.
In this post, we’ll examine the role of AI in software quality assurance, including benefits, use cases, and some important considerations to keep in mind.
In a rapidly growing and highly competitive software market, efficiency and cost reduction remain critical, as do accuracy and time-to-market for new products. Today, ten companies comprise the top players in software. The rest compete for the remaining 55% of market share, with a near-constant addition of new entrants. For all of these companies, failing to successfully integrate AI in software quality assurance could prove decisive.
Still, the move away from manual-intensive quality assurance still isn’t the rule for all organizations. Take software testing as a representative example. In a Gartner survey of 248 IT and software engineer leaders involved with automated software testing, only 56% use automation for API testing, while more resource-intensive areas like regression testing see even slower adoption:
Of course, testing is just one candidate for automation and AI augmentationand the benefits of AI extend to many other areas of quality assurance.
Sixty five percent of professionals in the software field already use AI in their software quality assurance processes (TestRail). Here are some good reasons why:
So where exactly does AI fit into quality assurance processes? Let’s look at some of the most impactful use cases that we see across the industry:
You can use AI to set up and optimize test infrastructure, deliver consistent requirements, and improve reporting. Using AI for test environment optimization can improve developer productivity, as developers will spend less time testing and more time innovating.
AI can also help organizations achieve better efficiency as they move testing earlier in the development cycle (shift-left testing), or incorporate Model-Based Testing (MBT) and Behavior-Driven Development (BDD) testing models.
AI can test for more scenarios than manual testing alone, while prioritizing the most important tests. New trends in intelligent software testing include self-healing test automation and applications for regression testing, in which test scripts are automatically updated when changes are made to the application. By detecting code changes and only running tests that are affected by these changes, AI can potentially help teams reduce time in CI/CD pipeline.
Tools like Claude 3.7 Sonnet and CodeRabbit can provide feedback and suggestions for improving code in real time, leading to faster delivery, reduced rework, and better code coverage and quality overall. However, they are most effective when used alongside human expertise to ensure context-specific accuracy.
The decision to feed your (or your customer’s) code to an algorithm shouldn’t be made lightly. Also, consider how well an AI-powered code review can account for your domain-specific knowledge (how code changes will affect a company’s systems and customers, for example).
Many quality assurance teams use AI to provide comprehensive analysis of user feedback. This includes sentiment analysis, topic modeling, and entity recognition (spotting recurrent mentions of specific features, bugs, or performance issues, for example), and feedback classification.
Because ML and AI are capable of learning from data sets, they’re suitable for a variety of predictive analytics use cases. Within DevOps environments, for example, you’ll find AI in use for advanced defect detection and predictive maintenance, helping to improve testing times and speed of delivery.
Continuous testing has become commonplace within software development, especially for organizations that rely on DevOps. Today, 80% of DevOps organizations integrate continuous testing, opening the door to AI integration with CI/CD pipelines and testing processes for quality assurance.
In these environments, developers may interact with AI-powered chatbots, external AI interfaces, or embedded AI tools in their daily work. As part of a continuous integration strategy, AI can be very effective for code quality control.
As we’ve discussed in our exploration of E2E web app testing, well-run E2E relies on test automation throughout its various phases. Increasingly, organizations are turning to AI to enhance these critical automations.
According to the 2024 World Quality Report, 71% of quality assurance teams integrate some form of automation alongside their manual testing capabilities. How concerned should software quality assurance teams be about automation and AI? As generative AI in particular gets better at handling various aspects of software testing, it may take more of that work off the hands of human workers.
Handing quality assurance over to AI outright leaves many teams understandably uneasy. At the end of the day, some form of human intelligence will be required to account for all that AI still cannot (providing domain-, industry-, or client-specific context and judgement, for example).
The more likely outcome is an upskilling of software engineers, in which the role of engineers evolve to work alongside—and maximize—AI tools. That is: a human-in-the-loop approach in which people provide guardrails against the potential pitfalls of such a heavy reliance on AI.
Despite its potential for operational efficiency, AI adoption still varies among software development teams. Some predict that the AI gap will quickly close—and soon—as more teams find innovative ways to integrate AI throughout the quality assurance process.
When working with companies in healthtech, fintech, and other industries, Transcenda begins with an assessment of current QA processes. Which AI tools does your team already use, and how do you integrate them into existing software development and quality assurance processes?
From there, we can review, suggest, and implement cases where AI would make sense to implement improvements. Read our overview of AI and ML in FinTech to get an idea of what this process looks like for one of the specific industries we serve. Or, explore our case studies for real-world examples of how we approach the design and engineering process overall.
In the end, AI’s transformative role in QA lies in its ability to enhance, not replace, human expertise. By strategically integrating AI into processes, organizations can achieve higher accuracy, faster delivery, and better product quality.
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