For two decades, mobile test automation has been built on a flawed assumption: that an app is a collection of XML nodes rather than a visual interf...
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This was an insightful read! Key takeaways that stood out to me:
Going forward, I aim to apply this mindset even without VLM tooling, by first expressing test cases in natural language to capture intent and expected user-visible outcomes before implementation. Decoupling the “what” from the “how” introduces a clearer specification layer and should improve both robustness and maintainability across test suites.
May i ask something! 🤔🤔
The shift described here -> moving from rule-based DOM parsing to perception-based reasoning is exactly where applied AI needs to go. As someone working heavily with agent behavior optimization and RLHF, I've seen firsthand how rigid frameworks fail when dealing with dynamic env. , trraditional testing forces an XML constraint on what's fundamentally a visual interface.
The way VLMs handle language alignment " mapping visual data directly into model's embedding space " solves the core issue of locator fragility. It makes complete sense that this approach pushes test stability to 95%+ , leaving the traditional 70-80% baseline behind. I am curious about the deployment side: when running parameter-efficient models like DeepSeek-VL2 for sub-100ms inference at the edge, how much fine-tuning or instruction tuning is typically required to prevent VLM from hallucinating intent when encountering highly customized or visually ambiguous UI components?
It's a good guess type questions, anyone experienced in fine tuning and RLHF presnet here?
So you know rlhf huh, do you think we will favor heavy reasoning models for complex flow, or can we squeeze enough juice out of edge models to handle long user journeys???????????????????????
how'd you guys know this much 🦞
As a CSE student, this really helped me understand how VLMs can solve the fragility of locator-based testing in a practical way. I also liked the part about writing test cases in plain English, Also I’m curious how reliable this is in very complex UIs or edge cases where visual elements look similar.
Thank you for sharing, this gave me a clearer picture of where testing is heading.
This is a really interesting shift in how we think about test automation.
The biggest pain point I’ve seen with traditional mobile testing is exactly what you mentioned locator fragility. Even small UI refactors end up breaking a bunch of tests that were technically still valid from a user perspective. It turns automation into a maintenance task instead of a productivity boost.
The idea of grounding tests in visual understanding instead of DOM structure makes a lot of sense, especially for catching layout or rendering issues that users actually notice. That said, I’m curious about a couple of things:
How reliable are VLMs when the UI is visually ambiguous (e.g., similar buttons, dynamic content)?
What does debugging look like when a test fails can teams trace why the model made a decision?
Feels like this could be a big step forward, but adoption will depend a lot on trust and transparency in how these models behave in edge cases.
May I ask something — how well do VLM-based tests perform when the UI has very subtle differences, like similar buttons or dynamic layouts across devices?
Honestly, this was a really interesting read. I’ve worked a bit with traditional mobile automation, and one of the biggest issues is how easily tests break with even small UI changes. Maintaining locators sometimes feels harder than writing the tests themselves.
The idea of VLMs understanding the screen visually instead of relying on XML structure makes a lot of sense. Writing steps in plain English and still having them work after UI updates sounds really practical.
I also liked the point about detecting visual bugs, since that’s something traditional automation usually misses.
One thing I’m curious about is how this handles performance and edge cases at scale — especially in apps with frequent UI experiments or A/B testing. But overall, this feels like a strong direction for the future of testing
What stood out to me is how VLMs don’t just “fix” testing issues like self healing locators,
but actually remove the root problem entirely moving from structure dependent automation to perception based testing feels like a fundamental evolution...... not just an upgrade
𝐇𝐚𝐯𝐞 𝐰𝐞 𝐛𝐞𝐞𝐧 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐰𝐫𝐨𝐧𝐠 𝐭𝐡𝐢𝐧𝐠 𝐚𝐥𝐥 𝐚𝐥𝐨𝐧𝐠?
What really clicked for me while reading this was the idea that traditional mobile testing focuses more on how the UI is coded rather than how the UI is actually experienced by users. Humans don’t see XPath, IDs, or XML trees - we see buttons, layouts, colors, spacing, and flow. That shift in perspective made this article genuinely interesting.
The biggest takeaway for me is that VLMs are not just “improving” locator-based testing, they’re changing the abstraction layer completely. Instead of teaching automation where an element exists in code, we’re teaching AI what the interface means visually. That feels like a major evolution rather than another automation upgrade.
I also liked the discussion around visual bugs and dynamic UI states. In many real-world apps, tests technically pass while the actual user experience is broken because layouts shift, rendering fails, or elements appear incorrectly. Traditional frameworks rarely catch those issues reliably.
The future of testing honestly feels less like scripting and more like building systems that can perceive and reason the way users do. Really insightful article.
Really interesting perspective—testing should reflect how users actually experience the UI, not just how it’s coded.
The shift toward visual understanding feels like a true evolution in automation, not just an improvement.
Totally agree — just because tests pass doesn’t mean the user experience is actually good. This shift from code-level checks to how things really look and feel seems like the missing piece.
One of the most interesting parts of this article is the idea that mobile automation has been solving the wrong problem for years - treating apps as XML structures instead of visual interfaces built for humans.
The distinction between “AI-assisted locators” and true Vision AI was especially strong. Most tools still depend on selectors underneath, while VLM-based testing changes the paradigm entirely by grounding automation in what users actually see.
I also liked the focus on visual bugs and dynamic UI states - the exact areas where traditional automation tends to fail despite tests technically “passing.”
This feels less like an incremental improvement to QA and more like a shift in how mobile testing will fundamentally work going forward.
This was an exceptionally well-written and timely piece on how Vision Language Models (VLMs) are reshaping the future of mobile test automation. What stands out immediately is that it goes beyond surface-level AI excitement and addresses the real structural problem many teams still face: fragile locator dependency and excessive script maintenance.
The explanation of traditional automation treating applications as XML structures instead of real user-facing interfaces was especially sharp. That framing captures why many legacy approaches struggle in dynamic modern apps, even when the product itself is functioning correctly. Shifting from element-based testing to visual understanding is not just a tooling upgrade—it represents a fundamental change in testing philosophy.
I also appreciated how clearly the article highlighted the move from implementation-focused testing to intent-driven validation. Using natural language instructions and visual context can significantly reduce maintenance overhead while making automation more accessible across teams, not just to highly specialized engineers.
Another strong point was the focus on user experience issues such as layout inconsistencies, spacing problems, and missing elements—areas that traditional functional tests often overlook. That perspective is critical because product quality is not only about whether features work, but whether they work well for real users.
The inclusion of the VLM landscape and benchmark comparisons added strong practical value. It gave readers a realistic view of where models like GPT-4o, Gemini, Claude, and Qwen fit depending on use case, scale, and performance priorities.
Overall, this is the kind of content that adds real value to the tech community: insightful, practical, forward-looking, and grounded in actual engineering challenges. Excellent work—highly relevant for anyone involved in QA, automation, or AI-driven product development.
This is a strong framing of the shift from locator-based automation to visual reasoning, and it highlights the real pain point most QA teams feel daily: tests that break for non-functional changes.
One nuance I’d add is that the real unlock isn’t just “screenshots instead of locators,” but the abstraction layer VLMs introduce between intent and UI state. That’s where the biggest long-term impact sits—especially for dynamic, A/B-tested mobile apps where the DOM is no longer a stable contract.
That said, the maintenance claims (near-zero) will likely depend heavily on how well edge cases are handled in production: things like ambiguous UI elements, visually similar components, or accessibility-driven layout shifts. Those are still open failure modes in current VLM systems.
Overall though, this direction feels less like an incremental improvement to test automation and more like a reset of the underlying model of how we define “what to test against.”
The transition from rule-based DOM parsing to perception-based reasoning represents a fundamental shift in how agents interact with digital environments, effectively moving from a world of rigid, fragile locators to one of semantic understanding. By mapping visual data directly into a model's embedding space, Vision-Language Models (VLMs) bypass the "XML constraint" that plagues traditional testing, allowing for a self-healing approach that pushes stability far beyond the 70-80% baseline. However, when deploying parameter-efficient architectures like DeepSeek-VL2 for sub-100ms edge inference, the challenge shifts from locator fragility to "intent hallucination," particularly when the model encounters custom or ambiguous UI components.
To mitigate this without the heavy overhead of full-parameter fine-tuning, the industry is increasingly leaning toward targeted instruction tuning and Direct Preference Optimization (DPO). Achieving reliability at the edge often requires a combination of visual token pruning—to focus compute on high-entropy UI regions—and self-consistency loops, where the model is prompted to generate an "atomic" description of a component before acting upon it. This internal reflection acts as a guardrail, ensuring that the model's mapping of visual data aligns with the actual functional intent of the interface, effectively bridging the gap between raw perception and reliable agent behavior in dynamic, real-world environments.
This was a genuinely great read it really shifted how I think about mobile app testing.
The thing that hit me most was the move away from locator-based automation. Anyone who's worked with Appium knows the pain: one UI tweak and half your test suite breaks. VLMs seem to sidestep that entirely by reading screens the way a person would looking at layout, visual context, and appearance instead of hunting for a specific element ID. Less flakiness, way less maintenance. That alone makes this worth paying attention to.
I also love the plain English test cases idea. Not everyone contributing to a product is technical, and right now that creates a real bottleneck in testing. If a product manager or designer can actually write and understand test cases, coverage improves and the whole team owns quality not just engineers.
And honestly? The dynamic UI handling might be the most underrated part. Popups, A/B variants, layout shifts — these are constant in real apps, and they're a nightmare to maintain scripts for. Not having to update everything every time something changes would be a massive time saver in practice.
My one lingering question: how do these models hold up in truly complex scenarios — heavy animations, real-time data feeds, that kind of thing? It would be fascinating to see some honest benchmarks and edge-case breakdowns, because that's usually where the gap between "promising technology" and "production-ready tool" becomes clear.
Great piece overall. Looking forward to seeing where this goes.
Interesting use case. VLMs in testing sound promising, but the real challenge will be how reliable they stay across different UI states and edge cases. That’s where most automated approaches usually struggle.
I think the locator problem framing is correct. Two decades of mobile test automation have been built on the assumption that a UI is an XML tree rather than a visual surface designed for human eyes. Every self-healing locator framework that came along was just a more sophisticated way of patching the same root problem without solving it.
Also, the Chandra parallel is worth noting here. A 5B model purpose-built for one visual task outperforming the 'generalist giants' isn't a coincidence. It's what happens when the model is actually trained for the problem rather than approximating it from general capability. The VLM approach in testing is the same logic applied to mobile, which is, stop asking a general model to guess at element structure and build something that sees the screen the way a user does.
However, the one thing I'd push on is that the near-zero maintenance claim is compelling, but it's doing a lot of work. Visual understanding handles UI structure changes well, but it gets harder when design systems shift significantly, brand refreshes, full layout overhauls, and dark mode rollouts. I'm pretty curious how the visual grounding holds up there versus incremental UI changes, which is where most of the day-to-day brittleness lives anyway
This is such a relevant topic for 2026. Mobile UIs change so frequently that maintaining test scripts becomes exhausting. Using VLMs to interpret screens more like a human tester could genuinely improve test stability. Curious to see how this performs in large-scale production apps.
Great insights on how Vision Language Models are redefining mobile app testing. The shift from locator-based automation to visual understanding feels like a fundamental paradigm change, especially considering how fragile traditional UI tests are with dynamic interfaces.
The idea of writing test cases in natural language and having the system interpret UI context visually is particularly impactful it lowers the barrier for collaboration across teams while improving stability and coverage.
Curious to see how this evolves further, especially in terms of scalability and real-world CI/CD adoption at scale. Definitely a space worth watching 🚀
This was a really insightful deep dive. The part that stood out most to me is how clearly it explains why traditional testing was never built for how modern apps actually behave.
The idea that for years we treated apps like XML structures instead of visual interfaces really hits. That explains why locator-based testing keeps breaking even when the app itself is working fine.
What I found most interesting is how VLMs shift testing from structure to perception. Instead of chasing element IDs, the model understands layout, context, and what the user actually sees. That feels like a more natural way to test apps, especially with dynamic UIs and frequent updates.
Also, the examples around detecting visual bugs and handling A/B changes show a gap that traditional automation doesn’t really cover well.
Overall, this feels less like an incremental improvement and more like a change in how we think about testing itself.
I’ve been diving into VLMs lately, and this really cleared up why my traditional automated tests always seem to break the second a UI dev changes a single ID.
What really clicked for me was the part about 'Visual Adapters.' I always assumed you had to use a massive model like GPT-4 for this to work, but seeing that smaller, optimized models (like Phi-4) can actually handle this with low latency is a game changer. It makes me wonder—if the AI is 'seeing' the screen like a human, does that mean we can finally stop worrying about the underlying code structure entirely?
I'm curious though, for someone just starting out with visual-first testing, how does the model handle 'busy' screens with a lot of animations or pop-ups? Does it ever get 'distracted' by things that aren't actually buttons?
Thanks for the deep dive, it’s definitely making me rethink how I approach my next project's test suite!
Excellent perspective on how VLMs can move mobile testing beyond brittle locator-based automation. I especially liked the emphasis on visual understanding over element dependency—this addresses not just flakiness but also the long-standing gap in catching UI/UX regressions that traditional automation often misses. The point about natural language-driven testing making automation more accessible to broader teams is particularly compelling. Also appreciated that the post balanced innovation with practical adoption through CI/CD integration rather than treating VLMs as a complete replacement overnight. Really insightful look at where intelligent test automation is heading.
A very interesting take on how Vision Language Models are reshaping mobile app testing. Shifting from traditional locator-based automation to visual-driven understanding feels like a significant advancement, especially when UI changes constantly make conventional tests unreliable.
The concept of describing test scenarios in natural language and allowing the system to interpret the interface visually is incredibly powerful. It simplifies collaboration between technical and non-technical teams while also making automation more stable and efficient.
It will be exciting to see how this approach performs at scale, especially within enterprise-level CI/CD workflows. This is definitely a trend that will have a strong impact on the future of testing.
Loved this article, really refreshing perspective on mobile testing. VLM-based automation feels like the future, especially with how it tackles flaky tests and visual bugs. Definitely inspired to explore this further.
Really insightful read 👏 The shift from locator-based testing to VLM-powered visual understanding feels like the future of mobile QA. Moving from AI that can only think to AI that can actually see app interfaces is a game-changer 🚀 Great breakdown!
This piece nails the core issue. The problem was never that locator strategies weren’t sophisticated enough, it’s that we were writing contracts with the implementation rather than the experience. A vision-based test breaks when the user experience breaks. A locator-based test breaks when a developer renames an ID. Only one of those failure modes is actually useful.
The 29 new bugs caught on live Play Store apps is the stat I’d anchor any internal conversation around. Real bugs, already in production, that existing automation had missed entirely. That’s the coverage gap made tangible.
This is a really clear and well-structured explanation of how mobile testing is evolving. I especially liked how you pointed out the “locator problem” — it’s something most teams face but don’t always question.
The comparison between traditional testing and VLM-based testing was also very practical, especially around flaky tests and maintenance issues. It gives a good real-world perspective, not just theory. It might be interesting to also touch a bit more on challenges like cost or implementation in existing workflows, but overall this is a strong and insightful write-up.
What stood out to me most was the distinction between “AI-assisted locator fixing” and true VLM-based testing.
A lot of tools claim to be AI-powered, but if they still depend on XPath, accessibility IDs, or DOM parsing, the core fragility is still there—just patched with smarter recovery. The explanation of Vision AI treating the screen the way a human tester does, based on layout, context, and visual understanding, makes that difference much clearer.
The point about writing tests like “Tap on the Login button” instead of binding everything to internal element structure is also a big shift in mindset. It moves automation closer to user behavior instead of implementation details.
I also liked the focus on visual bugs—misalignment, missing UI elements, rendering issues—which traditional locator-based automation often misses completely. That’s usually where users notice problems first.
This makes the discussion less about replacing Appium and more about solving the actual maintenance and reliability problem in modern mobile QA.
This was an interesting read—especially the shift from locator-based automation to vision-driven testing. The idea of writing tests in plain English and letting the model interpret UI context feels like a big step toward reducing maintenance overhead.
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What stood out is how this approach focuses more on validating user flows rather than just DOM-level interactions, which is where most flakiness usually comes from. At the same time, I’m curious how VLM-based testing performs in edge cases like highly dynamic UIs or personalized screens at scale.
Definitely feels like the direction mobile QA is heading, but still interesting to see how teams balance this with existing frameworks in production.
This was an interesting read—especially the shift from locator-based automation to vision-driven testing. The idea of writing tests in plain English and letting the model interpret UI context feels like a big step toward reducing maintenance overhead.
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What stood out is how this approach focuses more on validating user flows rather than just DOM-level interactions, which is where most flakiness usually comes from. At the same time, I’m curious how VLM-based testing performs in edge cases like highly dynamic UIs or personalized screens at scale.
Definitely feels like the direction mobile QA is heading, but still interesting to see how teams balance this with existing frameworks in production.
Great article! The framing of VLM-based testing as solving the "flawed assumption" that apps are XML trees rather than visual interfaces really resonates. The distinction between self-healing locators (which patch the symptom) and true visual understanding (which removes locator dependency entirely) is a crucial one that many teams miss when evaluating AI testing tools. The 91% edge-case accuracy stat and the real-world bug detection numbers make a compelling case that this is production-ready now, not just theoretical.
One of the strongest takeaways from this article is that mobile automation has been solving the wrong problem for years by treating apps as XML structures instead of visual interfaces. The shift from locator dependency to visual reasoning feels less like an upgrade and more like a paradigm change in QA. I especially liked how VLMs enable plain-English test instructions while still adapting to dynamic UI changes, popups, and layout shifts. Curious to see how teams measure long-term reliability and inference cost when scaling VLM-based testing across large production apps.
This is a strong and timely perspective on how testing paradigms are evolving. The shift from DOM/locator-centric automation to visually grounded reasoning is not just an incremental improvement—it addresses a foundational mismatch between how apps are built (for humans) and how they’ve traditionally been tested (for machines).
What stands out is the practical impact: reduced flakiness, lower maintenance overhead, and improved detection of visual regressions—areas where conventional frameworks consistently struggle. The emphasis on intent-driven testing (“tap login”, “validate cart price”) also signals a meaningful move toward democratizing test creation across teams, not just QA specialists.
That said, it’ll be important to see how VLM-based systems handle edge cases at scale—such as accessibility states, localization variations, and performance under constrained mobile environments. Additionally, governance around model decisions (explainability, reproducibility in CI) will be critical for enterprise adoption.
Overall, this is a compelling direction. If the reliability and cost-efficiency claims continue to hold in broader production settings, VLM-driven testing could realistically become the new default for mobile QA.
Great breakdown of why locator-based testing has been a leaky abstraction all along — the framing of "apps are visual interfaces, not XML trees" is the right mental model shift.
One thing I'd love to see explored further: the failure mode profile of VLM-based agents vs. traditional automation. Locator-based tests fail loudly and predictably (element not found, selector broken). VLMs, being probabilistic, can fail silently — misidentifying a UI element with high confidence, especially on novel or unconventional UI patterns. How does Drizz handle uncertainty quantification or confidence thresholding before acting on a visual interpretation? Is there a fallback mechanism, or does it rely purely on the model's output?
Also curious about the latency implications in CI/CD at scale. The article mentions parameter-efficient models achieving sub-100ms inference — but when you're running 20-30 critical test cases end-to-end on real device farms, the cumulative VLM inference cost (per screenshot, per action) adds up. How does this compare to Appium-based runs in practice for a mid-sized team?
The 29 new bugs found on Google Play apps stat is compelling. Would be interesting to know the false positive rate alongside it — that's usually where AI testing tools lose team trust over time.__
This article clearly explains how mobile app testing is evolving beyond traditional DOM-based approaches. The use of Vision Language Models to understand UI elements like humans is very interesting. I think this can reduce failures caused by UI changes and make testing more reliable. As someone learning data and analytics, I find this shift towards intelligent systems very exciting.Also, as a student learning data and analytics, I find this direction very exciting.
This actually hit a bit too real 😅
Maintaining locators sometimes feels harder than writing the tests themselves, especially when UI changes are frequent.
The idea that apps were always treated like XML instead of visual interfaces explains a lot of the pain. VLMs making decisions based on what’s visible instead of IDs just feels like the right direction.
Would love to see how this performs in messy real-world apps with lots of dynamic UI.
Strong insight—this really highlights the shift from structure-based testing to visual, human-like understanding.
The biggest win is eliminating brittle locators, but I’m curious about edge cases—like ambiguous UI elements or debugging when the model makes a wrong decision.
Still, the move to natural language tests + visual reasoning feels like a real paradigm shift, not just an improvement.
“I found the shift from locator-based testing to VLM-driven understanding really interesting. The idea that models can interpret UI context like a human could significantly reduce maintenance overhead. Do you think this approach can fully replace tools like Appium in the future, or will it complement them?”
What I found most interesting is that VLM-based testing doesn’t just improve automation, it actually changes what we consider reliable testing. Using tools like Selenium or Appium, we’re tightly coupled to the UI’s structure, so even minor changes break tests.
VLMs shift this to a perception based approach, where the system understands intent from the visual interface itself. That feels much closer to real user behavior, which is probably why it reduces flakiness so effectively.
It also makes me believe that understanding user processes and intent may be more important in future QA positions than coding exact criteria. I'm interested in how this strikes a balance between cost and performance in large-scale testing settings.
This was a really insightful shift in perspective—especially the idea that mobile testing has been built on the assumption that apps are “XML trees” instead of visual systems.
The explanation of how vision-language models bridge perception + reasoning was very clear. Models like GPT-4 evolved reasoning, but adding vision (as seen in VLM architectures like LLaVA or BLIP-2) feels like the missing piece for UI-driven systems like mobile apps.
The biggest takeaway for me was how this removes the root cause of flakiness—not just improving locators, but eliminating the dependency entirely. Traditional approaches (even “self-healing”) still operate within the same fragile abstraction layer.
The point about visual bug detection is also underrated. Most automation suites validate functionality but completely miss layout regressions, alignment issues, or rendering inconsistencies—which are often the most visible problems to end users.
One thing I’m curious about:
In cases with highly dynamic or low-contrast UIs (e.g., icon-heavy apps, animations, or dark mode variations), how do VLMs maintain consistency in element recognition across different visual states? Overall, this feels less like an incremental improvement and more like a paradigm shift—moving from DOM-based automation to perception-based testing.
This is a really strong articulation of why locator-based testing has been fundamentally misaligned with how users actually experience apps.
What stood out to me is the shift from structure-driven automation → perception-driven automation. Traditional frameworks assume the UI is an XML tree, but in reality, the user interacts with visual intent (buttons, hierarchy, spacing). VLMs finally align testing with that reality.
One interesting angle I’d love to see explored further is robustness across fragmented ecosystems, especially Android. Since VLMs rely on visual understanding, variability in UI (OEM skins, screen densities, inconsistent design systems) could introduce new challenges—even if they solve locator brittleness. There’s already some anecdotal evidence that models perform unevenly across platforms, which suggests dataset bias might become the new “flakiness” layer to solve.
Also, the claim of “near-zero maintenance” is compelling—but I wonder if maintenance simply shifts from test scripts → model behavior tuning, prompt design, and edge-case handling. In other words, are we eliminating maintenance or redefining it?
That said, the biggest unlock here feels cultural rather than technical:
non-engineers being able to contribute to test coverage via natural language. That could fundamentally change how QA scales in fast-moving teams.
Curious to see how this evolves—especially once teams start combining VLM-based testing with CI feedback loops and production telemetry.
We’ve been building automation around the assumption that UIs are structured trees (XML/DOM), while users interact with visual intent. That mismatch is exactly why locator-based testing has always been fragile by design. VLMs finally align testing with how software is actually experienced: visually and contextually.
What’s powerful here isn’t just “AI replacing locators”—it’s the shift to perception-driven automation:
tests are no longer tied to how the UI is implemented, but to what the user sees and means. That fundamentally removes an entire class of failures rather than patching them (like self-healing locators tried to do).
The real unlock, in my opinion, is not just stability—it’s abstraction:
moving from code → intent (“tap login” instead of finding IDs)
moving from engineer-owned testing → team-wide contribution
moving from brittle scripts → adaptive systems
That said, I don’t think “near-zero maintenance” means no maintenance—it likely shifts:
from fixing selectors → managing model behavior (prompt clarity, edge-case ambiguity, visual similarity conflicts, etc.).
In other words, we’re trading deterministic fragility for probabilistic reasoning—which is powerful, but introduces a different kind of engineering discipline.
Also curious about:
disambiguation when multiple similar elements exist
performance trade-offs in CI at scale
and whether dataset bias becomes the new source of “flakiness”
Still, this is the first approach that actually tackles the root problem instead of optimizing around it.
If it holds up in large-scale production, this could redefine how QA is done—not just improve it.
This was a really interesting read. The idea of testing based on how a user actually sees and interacts with the app — instead of relying on IDs or XPath — makes a lot of sense.
In most projects, tests don’t fail because the feature is broken, they fail because the UI changed slightly. That’s always been frustrating. Using vision + language to understand intent instead of structure feels like a much more practical approach.
I also liked the point about reducing flakiness and maintenance. Writing tests is one thing, but keeping them working over time is the real struggle — and this seems like a solid step in fixing that.
Curious to see how this handles highly dynamic apps or personalized UI flows, but overall this feels like a direction that can genuinely change how testing is done.
Great article 👍
This article makes a really strong point that most teams still ignore: the real problem in mobile testing isn’t lack of automation, it’s locator dependency.
We’ve normalized spending more time fixing tests than validating product quality.
The line about apps being treated as “XML nodes instead of visual interfaces for human eyes” was honestly the biggest insight for me. That perfectly explains why traditional Appium/Selenium-style automation keeps failing even when the app itself works fine. VLMs shifting testing from element detection to visual understanding feels less like an improvement and more like a paradigm shift.
What stood out most was the idea that QA can move from “script maintenance” to actual “quality assurance” again—especially when tests are written in plain English and adapt to UI movement naturally. Research-backed gains like ~9% higher code coverage and detection of previously missed bugs make this much more than just AI hype.
One question I’d love your take on:
How do you see VLM-based testing handling highly regulated flows (banking, healthcare, fintech) where auditability and deterministic reproducibility matter as much as adaptability?
Honestly, the biggest shift here feels less about AI and more about how we think about testing.
We’ve always been testing the structure (IDs, locators, XML), but users interact with what they see. So it kind of makes sense why tests break so often even when the app is fine.
VLMs fixing that by focusing on visual context instead of implementation feels like a more natural approach.
That said, I’m curious about the trade-offs. With locators, failures were pretty clear — something changes, test breaks. But with VLMs, it feels a bit more fuzzy: like how does it handle similar-looking elements, or different UI variations across devices?
Also wondering if “low maintenance” just shifts into things like prompt tuning or handling edge cases differently.
Still, this feels like a genuine step forward instead of just patching the same problems (like self-healing locators did). And the fact that tests can be written in plain English is honestly a big deal for team collaboration.
Curious to see how this holds up in real-world messy apps.
Really insightful perspective. The move from fragile, locator-based testing to vision-driven automation feels like a major leap forward for QA. If VLMs can consistently interpret UIs the way humans do, it could significantly reduce maintenance overhead and speed up testing cycles. Curious to see how this evolves, especially around edge cases and integration into existing workflows.
What really blew my mind here is how VLMs completely change the way we think about automation.
The idea that you can simply say “tap the login button” and the system actually finds it visually on the screen—not via brittle locators or IDs—is a huge shift. It’s not just executing commands, it’s understanding the UI the way a human would.
This makes tests far more resilient to UI changes and dynamic layouts, which has always been one of the biggest pain points in mobile automation.
If this scales well, it could seriously reduce maintenance overhead and make test creation accessible beyond just developers. Definitely feels like a step toward more human-like, intent-driven testing.
This is a really insightful take on how vision language models (VLMs) are transforming mobile test automation. The idea that traditional testing has been built around XML structures rather than visual understanding really stood out. It clearly explains why locator-based approaches often feel fragile in modern, dynamic UIs where layouts and element hierarchies frequently change, even when functionality remains the same. This gap between how tests interpret an app and how users actually experience it has always been a key limitation of conventional automation.
What I found most compelling is the shift from implementation-based testing to intent-based testing. Moving from rigid, locator-driven scripts to natural language instructions like “tap the login button” feels like a major step forward. By grounding execution in visual context, VLMs introduce flexibility and adaptability that traditional frameworks lack. This not only reduces maintenance overhead but also makes test creation more accessible to a broader range of team members.
Another important highlight is the ability of VLMs to detect visual issues that traditional automation often misses. Problems like layout misalignment, missing elements, or inconsistent spacing directly impact user experience but are rarely captured by purely functional tests. Enabling systems to “see” interfaces helps bridge this gap and aligns testing more closely with real user expectations.
It would be interesting to see how this approach performs at scale, especially in complex applications with visually similar components. Overall, this feels like a foundational shift rather than a minor improvement, and it has strong potential to redefine how modern testing is approached.
The transition from traditional, locator-based testing to VLM-powered automation represents a significant evolution in software engineering. Relying on brittle XML nodes has long been a maintenance burden. It is compelling to see how integrating computer vision with LLMs enables testing agents to "see" and reason about a UI, effectively mimicking human perception. This not only minimizes flaky tests but also allows for describing test intent in natural language, which is far more intuitive. This approach bridges the gap between static frameworks and dynamic user experiences, making it a vital advancement for robust, modern mobile app development.
This was such an eye-opener! 😲 I never realized that traditional mobile testing was basically just 'blind' to how humans actually see the app. The idea that we've been testing XML nodes instead of the actual visual screen makes so much sense now—that’s exactly why my tests used to break whenever a developer changed a button ID! 💔
The part about writing tests in plain English like 'Tap the login button' instead of coding complex scripts feels like a total game-changer for beginners like me. 🚀 It really lowers the barrier to entry for testing. I’m also super curious about how these models handle tricky situations, like when two buttons look almost identical or during dark mode switches. It feels like we are finally moving from 'fixing broken scripts' to actually 'ensuring quality.' Can’t wait to see this evolve! 🔥
Really insightful read! I found the way VLMs bridge visual understanding with automated mobile app testing especially interesting. Their ability to interpret UI elements contextually can significantly improve test accuracy and reduce manual effort. This could be a game-changer for scalable QA in modern app development. Excited to see how this evolves further!
Really interesting perspective. While building my Android app, I saw how easily small UI changes break locator-based tests. VLMs feel more natural since they understand screens the way users do, not just XML. If this becomes lightweight and affordable, it could seriously improve testing for developers like us.
The discussion highlights a clear inflection point in mobile test automation, where Vision Language Models (VLMs) are not just improving existing approaches but fundamentally redefining them. Traditional locator-based testing, tightly coupled to DOM structures and implementation details, has long struggled with fragility and high maintenance often failing even when the user experience remains intact. VLMs shift this paradigm by grounding testing in visual perception and user-observable behavior, enabling systems to interpret UI context more like a human rather than relying on brittle selectors. This transition from structure-dependent automation to intent-driven, perception-based validation reduces maintenance overhead while improving the ability to detect real-world issues such as layout inconsistencies, rendering bugs, and visual regressions. Moreover, the ability to express test cases in natural language introduces a more accessible and collaborative layer across teams, decoupling the “what” from the “how.” While questions around scalability, ambiguity handling, and debugging transparency remain important for widespread CI/CD adoption, it’s evident that this is not merely an incremental upgrade but a foundational shift in how software quality is defined and validated in modern, dynamic applications.
Brilliant breakdown of how VLMs are completely flipping the script on mobile app testing!
What stood out to me most is the fundamental shift in how we perceive app interfaces. For two decades, we've treated mobile apps as brittle XML trees rather than visual experiences designed for human eyes. The "Locator Problem" has been the bane of every QA engineer's existence—teams end up spending way more time maintaining and fixing flaky tests due to minor UI refactors or iOS/Android inconsistencies than actually expanding test coverage.
The fact that VLMs (like the tech powering Drizz) can process a screen holistically and understand intent through plain English commands like "Tap on Instamart" is a massive leap forward. By decoupling tests from fragile element IDs, we can finally handle dynamic UIs, popups, and A/B tests gracefully.
Furthermore, catching those subtle visual regressions (layout shifts, missing elements) that traditional automation entirely misses bridges a critical coverage gap. The research noting that VLM-based systems caught 29 new bugs on Google Play apps that existing tools missed speaks volumes about its real-world impact.
We are finally moving from "telling the code where to click" to "telling the AI what to achieve." Thanks for sharing such an insightful read!
Really liked this breakdown. The shift from locator-based testing to actually understanding the UI visually makes a lot of sense. Most of the issues I’ve seen in testing come from small UI changes breaking everything, even when the app works fine.
The idea of writing tests in plain English and letting the model figure out the UI feels like a big step forward, especially for reducing flaky tests and maintenance.
One doubt though , how do VLMs handle cases where multiple buttons look similar or the UI is slightly ambiguous?
Strong piece—especially the idea that testing should shift from a DOM-first view to a human-perception-first one. That’s a real paradigm change, not just a tooling upgrade.
One thing worth emphasizing: VLMs don’t just fix flaky locators—they redefine tests around user intent. But since visual reasoning is probabilistic, handling false positives in critical flows becomes a key challenge.
Also, a hybrid approach (VLMs + deterministic checks) feels more practical than full replacement, especially given latency and cost constraints.
Overall, compelling direction—would be even stronger with real-world benchmarks and failure cases.
Interesting perspective—especially the shift from a DOM-centric approach to something closer to human visual perception. That feels like a genuine change in how we think about testing, not just better tooling.
What stands out is how VLMs move testing toward intent-level validation rather than element-level checks. At the same time, since visual reasoning is probabilistic, handling false positives in critical paths becomes an important challenge.
In practice, a hybrid model (VLMs alongside deterministic checks) seems more realistic than a full replacement, particularly given latency and cost constraints.
Would love to see more concrete benchmarks or real-world failure cases to better understand production readiness.
Really interesting take on how VLMs shift testing from locator-based logic to actual visual reasoning. The example of tests still working even when UI elements move or IDs change really highlights how brittle traditional automation has been for years.
What stood out to me is the “write tests in plain English” angle — it’s not just about convenience, it actually changes who can contribute to testing. That could seriously reduce the gap between QA, devs, and even product folks.
That said, I’m curious about edge cases — like how reliable this approach is with highly dynamic UIs, animations, or visually similar elements. Also wondering about performance trade-offs compared to traditional frameworks.
Overall though, this feels like a real shift rather than just another “AI wrapper” on top of existing tools.
Honestly, this hits a real pain point—UI changes breaking tests for no real user impact.
Moving from DOM-based checks to understanding what’s actually visible feels way more aligned with real-world usage. If VLMs can cut down flaky tests and constant locator fixes, that’s a huge win for anyone doing automation at scale.
Feels less like a feature and more like where testing should’ve been heading all along.
Testing apps used to be like trying to find a light switch in the dark by feeling the walls. If someone moved the switch an inch, you’d be lost. Now, AI gives the computer eyes. Instead of guessing where buttons are based on hidden code, the AI just looks at the screen like a person does.If it looks like a button, the AI clicks it. If the app looks messy or broken to a human, the AI flags it. It’s a huge shift from "reading code" to "actually seeing" the app.
Honestly, the part that got me thinking was the framing around why locator-based testing breaks — not just that it breaks. The root issue is that we've been testing a visual product by querying its underlying XML structure, which was never designed to be stable. It's almost like writing accessibility tests by reading compiled bytecode.
The flaky test problem makes a lot more sense through this lens. It's not just a tooling issue, it's a mismatch between what the test sees and what the user sees.
Curious about one thing though — how does VLM-based testing handle apps where the visual design is intentionally inconsistent across user segments (heavy A/B testing, regional variants, etc.)? Does the model need to be re-calibrated per variant, or does it generalize well enough from the visual context alone?
That feels like the real stress test for this approach in production.
Interesting take✨, but I think the real shift isn’t just “VLMs replacing locators” — it’s the abstraction of testing from implementation to perception.
For years, we’ve been validating apps based on how they’re built (DOM, IDs), not how they’re experienced. That’s why even perfectly working apps fail tests.
VLMs flip that model by aligning testing with user-visible truth, which is closer to actual product quality.
That said, I’m curious about failure modes — especially in cases of visually similar components, heavy animations, or inconsistent design systems. If VLMs can handle those reliably at scale, this could genuinely redefine test stability
Really interesting perspective on how Vision Language Models are reshaping mobile testing. The shift from fragile locators to understanding UI context like a human is a big leap.
Focusing on intent over selectors could significantly reduce flakiness and make tests more resilient to UI changes.
Curious to see how VLMs handle edge cases and explain failures—but overall, this feels like the future of intelligent test automation.
Honestly didn't expect a technical blog to make me stop and think, but here we are.
I'm contributing to an open-source mobile project as part of Social Summer of Code, and the locator problem you described is something I've been quietly suffering through for weeks. Tests going red, spending an hour tracing the issue, and realizing — the app is fine. A button just got renamed.
What clicked for me reading this: we've been automating the wrong thing. Not the experience, just the structure underneath it.
Still wrapping my head around VLMs practically, so genuinely curious —
Is this something a contributor without a QA background can realistically pick up? Or does the plain-English part sound simpler than it is?
Either way, this one's bookmarked. Good stuff. 🔖
Wait… are we finally done babysitting flaky locators? 👀
For the longest time, mobile testing felt like we were fighting the UI instead of validating it… constantly fixing locators instead of focusing on real user experience. The way VLMs shift testing from structure to perception honestly feels like a mindset change, not just a tech upgrade.
What stood out to me was the idea of writing tests in plain English and letting the model see the screen like a human. That could seriously reduce the entry barrier for teams and speed up iteration cycles 🚀
Curious though — how well do these models handle edge cases like very similar UI elements or dark mode variations? Feels like that’s where real-world complexity kicks in.
Really excited to see where this goes in the next couple of years 🔥
This article does an excellent job of highlighting the fundamental shift from locator-based automation to visual-first testing powered by Vision Language Models. The explanation of how VLMs interpret UI context like a human tester really stands out, especially in addressing long-standing issues like flaky tests and high maintenance overhead. The comparison of different VLM architectures and their trade-offs adds valuable technical clarity, making the content useful for both beginners and experienced engineers.
What makes this particularly impactful is the emphasis on real-world outcomes—better test stability, improved bug detection (especially visual regressions), and faster testing cycles. The idea of writing tests in natural language instead of code is a game changer for collaboration across teams. Overall, this piece clearly shows why VLMs are not just an upgrade but a necessary evolution for modern mobile app testing.
What a brilliant and timely piece! In the past, mobile test automation has suffered from flaky locators, brittle XPath/CSS selectors, and a maintenance nightmare each time a designer tweaks a button or the UI goes through an A/B test. The fundamental assumption that apps are mere hierarchical XML/JSON structures rather than visual experiences designed for humans was always broken—and Vision-Language Models (VLMs) feel like the first genuine paradigm shift that attacks this problem head-on.
I love how the article breaks down the evolution from text-only LLMs to multimodal VLMs that can actually “see” the screen, analyzing layout, context, colors, icons, and spatial relationships like a human tester would. The power of being able to express tests in plain natural language, like "Add the first product to the cart and verify the total matches the item price," and then have the agent execute that test visually is incredible. Not only does it dramatically cut down on maintenance, but it also opens the door for non-QA folks (PMs, developers, even designers) to contribute to test coverage.
The stats are compelling too: 9% higher code coverage and 29 new bugs found (19 confirmed fixed) on real Google Play apps that traditional tools missed. This demonstrates a huge blind spot for locator-based or even simple self-healing approaches: they still struggle with visual regressions, layout shifts, dynamic content, and accessibility issues that only a vision-based system can reliably catch.
Honestly, this reframed something I've been vaguely frustrated about for years but couldn't articulate.
We've been writing tests that validate code structure, not what the user actually experiences. A button could be completely invisible or broken visually, and a locator-based test would still pass because the element ID exists. That's not testing — that's false confidence at scale.
The architectural breakdown you laid out really clicked for me — especially the distinction between fully integrated models like GPT-4V versus parameter-efficient ones like Phi-4 Multimodal hitting sub-100ms inference. That tradeoff between reasoning depth and real-time deployability is something most "AI in testing" conversations completely skip over.
And the 29 undetected bugs on Google Play — 19 of which were confirmed and actually fixed by developers — that's not a minor stat. Those weren't missed because teams were careless. They were missed because locator-based tools are structurally blind to visual failures. No amount of better scripting was ever going to catch them.
The self-healing locator point also landed. A lot of teams think they've "solved" brittleness by adopting self-healing frameworks, but if you're still operating on element trees underneath, you've just delayed the same problem. The root cause was never the broken selector — it was the dependency on selectors at all.
And the plain English test writing thing — I think people are underselling how big that shift actually is. The moment a PM or designer can write "tap on Instamart, add the first product to cart, validate the price" without an engineer translating it into code, quality stops being a bottleneck and starts being a team sport.
Been following the VLM space for a bit but hadn't seen it applied to mobile testing this concretely. Really good piece — thanks for writing it.
This was a really insightful read. What stood out to me most is how clearly it explains the core problem with traditional testing — it focuses on XML structure instead of actual user experience. That’s exactly why tests break even when the app is working fine.
The shift to Vision Language Models feels like a major change, not just an improvement. Understanding the UI visually and writing test cases in plain English makes testing more practical and less dependent on developers.
I also liked the point about detecting visual bugs, which traditional automation often misses. I’m curious though — how well does this approach handle cases where multiple UI elements look very similar or in highly dynamic apps?
This article does a great job articulating why locators are fragile, but I want to highlight something often missed in this conversation:
VLMs and locator-based approaches aren't actually solving the same problem set. Traditional automation catches logical bugs; VLM testing catches visual and UX bugs that slip through. The real power isn't replacing one with the other—it's understanding when you need each.
For example: A button that's visually perfect but logically broken (doesn't trigger the right API call) would pass a VLM test but fail a functional test. Conversely, layout shifts and visual regressions slip through traditional automation entirely.
So before teams jump to "VLMs are the future," I'd ask:
How are you currently losing the most bugs? Visual regressions or logical failures?
Does your test suite need to catch UX issues, or are those caught by design QA?
What's the ROI of adding VLM testing as a layer vs. replacing existing tests?
The 50%+ reduction in QA maintenance is compelling—I'm genuinely curious whether that holds for teams that need both visual and functional coverage, or if it's lower for hybrid approaches. Anyone here running both in parallel?
This is a compelling take on the evolution of mobile test automation, highlighting how Vision Language Models (VLMs) are shifting the focus from fragile, locator-based testing to a more human-like understanding of UI through visual context. By addressing long-standing issues like flaky tests caused by UI changes, VLMs have the potential to significantly reduce maintenance effort and improve test reliability while accelerating release cycles. The reported gains in accuracy and efficiency make this approach especially promising, though it will be important to see how challenges such as cost, scalability, and integration with existing frameworks are handled as adoption grows.
Also curious about one challenge: how do VLMs perform in multilingual apps, accessibility-heavy interfaces, or highly similar UI layouts where visual ambiguity is high?
This is exactly the evolution mobile testing needed less maintenance, more intelligence.
Vision-based automation feels like the future !!!
thankyou for this wonderful read about video language models