How multimodal AI is reshaping software testing

Picture this: You’re creating test cases for a new feature. You have a Jira ticket with text requirements, a Figma mockup from design, a workflow diagram from the architect, and a screenshot from a stakeholder meeting. Traditionally, you’d manually translate all of this into test steps: describing the UI in words, interpreting the diagram, cross-referencing the mockup.

But what if your testing tool could “see and “understand” all these artifacts directly, just like you do?

Just as humans rely on multiple senses (seeing, hearing, touch) to understand and act on the world around them, multimodal AI gives machines the ability to process diverse forms of input simultaneously.

This has far-reaching implications across many domains, but it’s particularly significant for software testing. Testing relies on understanding requirements, interpreting user interfaces, and identifying subtle variations or defects. These tasks become far more efficient when AI has access to the full spectrum of input data, not just text.

With the ability to interpret diverse data inputs, multimodal AI could be used to generate test cases that cover a wider range of scenarios and edge cases, leading to more comprehensive test suites.

What is multimodal AI? A quick primer

Multimodal AI refers to models that can process and combine different types of data (known as ‘modalities’) such as text, images, audio, video, and even time-series data like system logs or financial charts. It then translates them into actionable outputs like test cases, automation scripts, or defect reports.

This represents a major departure from earlier AI models that focused exclusively on a single type of input, typically text. With multimodal AI, different types of information are no longer processed in isolation. Instead, they are brought together, providing richer context and enabling more nuanced reasoning.

This convergence is not just a technical improvement, it’s a conceptual shift. To understand it, consider how humans use multiple senses. When we read a product specification and look at a screenshot, we integrate those inputs to understand the intent. When we listen to a colleague explain a workflow while drawing on a whiteboard, we interpret their voice and the diagram together. Multimodal AI aims to replicate that same integrated understanding, with each modality acting like a sense that broadens the model’s perception.

How multimodal AI became synonymous with AI

The widespread adoption of foundation models like GPT-4 accelerated this shift, opening the door for more natural and lifelike interactions. These advances have become the foundation for what we now call AI agents — systems that not only respond, but act autonomously, reason across contexts, and learn continuously.

In fact, multimodality has become the standard. All major foundational models released since GPT-3.5, including Anthropic’s Claude, Google’s Gemini, Meta’s LLaMA, and OpenAI’s GPT series, are inherently multimodal. Multimodal AI is no longer emerging technology — it’s the baseline. The real differentiator is how effectively organizations take advantage of that capability when they implement solutions like software testing platforms.

Visual input delivers high-value insight

The power of visual input cannot be overstated. A single image can carry dense layers of information, equivalent to hundreds of words or, in AI terms, roughly 700 tokens. That’s why the common phrase ‘a picture is worth a thousand words’ resonates so strongly in this context. Whether it’s a flowchart showing system logic, a dashboard with real-time metrics, or a UI mockup, visual artifacts offer insight that would be tedious and error-prone to express in text. AI that can interpret these artifacts is significantly more valuable than one that cannot.

Looking further ahead, there’s potential for multimodal AI to assist in early defect detection. An agent could ‘look’ at a UI and detect anomalies (for instance, broken layouts, incorrect fonts, or inconsistent spacing) that would otherwise require manual inspection. While the risk of false positives remains a challenge, the foundation for visual defect spotting is already in place. And as models improve, they’ll get better at distinguishing meaningful issues from harmless variations.

Multimodal AI in action: Tosca’s visual intelligence for test automation

In the world of software quality assurance, these developments matter more than most teams may yet realize. Requirements documentation is no longer purely textual. It includes screenshots, wireframes, mockups, architecture diagrams, and even photos of whiteboards from sprint planning meetings.

When a test automation agent is multimodal, it can consume both visual inputs and accompanying written requirements, then generate relevant test cases from both. This enables testing activities to begin before a single line of code is written, allowing teams to shift left with confidence.

How Tosca Vision AI works:

• Trained on millions of UI elements: Combines computer vision with neural networks to automatically identify and interact with interface objects such as input fields, labels, tables, trees, and buttons
• Real-time visual processing: Analyzes 40-60 screenshots per second (similar to self-driving car technology) to identify and interact with screen objects on any visible desktop application
• Seamless integration: Works within Tosca’s standard module and scanning interface alongside traditional scanners like API or web scanners
• Flexible deployment: Can be strategically combined with other Tosca engines based on specific needs, particularly valuable for applications handling sensitive data
• API-driven automation: Fully automatable through API-driven test case creation, with ongoing development leveraging large language models to automatically generate tests from requirements

Vision AI’s multimodal capabilities extend beyond UI testing. By connecting the dots between diverse types of data such as interaction logs, diagrams of expected workflows, performance charts, and visual mockups, Vision AI enables more comprehensive test coverage than text-based approaches alone.

How should quality engineers prepare for a multimodal future?

For organizations exploring AI-powered QA solutions, multimodal capabilities shouldn’t be seen as a luxury or futuristic feature. They are quickly becoming a core requirement.

And for software testing practitioners, multimodal brings an extra level of complexity. Here are three things quality engineers should look at to acquire the necessary new skills:

1. Understand the fundamentals of how these models work. Engineers don’t need to become data scientists, but a working knowledge of how multimodal models are trained, what kinds of data they use, and where they can go wrong is critical.
2. Get comfortable with AI-driven testing tools. As AI becomes a core part of applications, it will also play a larger role in the testing process itself. Engineers who can effectively work with AI-augmented tools like the ones Tricentis offers will be better positioned to keep up with rapid software evolution.
3. Prepare to collaborate. Multimodal applications are often built by cross-functional teams that include designers, AI engineers, product owners, and developers. Quality engineers must be proactive in these discussions, raising testing concerns early and often.

Multimodal AI is the new standard in QA

Multimodal AI gives agents both ‘the brain and the eyes.’ And it’s not limited to just text and images. Other modalities, like audio, are already part of the ecosystem, even if they’re less common in day-to-day testing workflows. While voice is often treated as a proxy for text, it carries additional information like intonation, pauses, and cadence. In future scenarios, agents could process recordings from daily standups or design discussions to extract requirements or generate questions for clarification.

This is the new baseline for building intelligent systems that can understand the complexity of modern software environments.

For QA professionals, embracing this shift is not just about keeping up, it’s about unlocking new levels of productivity, insight, and precision in testing. As testing moves from reactive validation to proactive prevention, multimodal AI is a foundational element that will make it possible.

Watch a demo of Tosca Vision AI and learn more about AI-driven test automation based on visual cues.