Oh, absolutely! So, during the session , I got a firsthand look at how MCP can really make life easier for testers. Here’s the thing when you feed your acceptance criteria or user stories into MCP, it doesn’t just spit out a basic test case. It actually generates multi-step test scenarios that feel surprisingly thorough.
What I found really cool is that it often highlights edge cases I might have completely overlooked. For example, in complex apps with lots of moving parts, you could easily miss certain conditions or user paths, but MCP tends to catch those. It’s like having a testing sidekick that nudges you, “Hey, don’t forget this scenario!” Honestly, it saved me a ton of time and gave me more confidence that I wasn’t missing anything important.
If you’re dealing with detailed acceptance criteria or trying to cover all your bases quickly, MCP is a game-changer.
Oh yes, I remembe, it was super insightfulsession! So, when it comes to debugging the AI’s decisions, the key takeaway for me was that it’s all about following its reasoning. Basically, you want to log what the AI is thinking and track the context it’s using for each suggestion.
For example, if it comes up with a test case that doesn’t make sense or suggests a fix that feels off, the trick is to dig into why it made that choice. By looking at its chain of thought, you can figure out whether the problem was with the prompt, the constraints you set, or something else. Once you spot that, you can tweak the prompts or adjust the rules, and you’ll see its suggestions start aligning a lot better with what you actually need.
It’s like having a teammate who’s learning as they go, you just need to peek over their shoulder, understand their reasoning, and guide them back on track.
Absolutely! Having attended Viktoria Semaan’s session at Testmu 2025, here’s how I’d put it in a more down-to-earth, practical way:
Think of your AI sidekick as something that “gets” your product environment without needing you to be an ML expert. In practice, it’s mostly about setting up the context—basically teaching the sidekick how your app works, what the key user flows are, and what rules it should follow. Once you define these, the sidekick can start helping you test smarter, adapt to different scenarios, and even point out edge cases, all without you writing complex ML models.
What I loved about the session was how approachable this is for QA teams, if you can map your product flows and rules clearly, you can start seeing real benefits immediately. It’s like giving your testing process a brain that learns your patterns, but without having to become a data scientist overnight.
Oh, that session by Viktoria was super insightful! When it comes to making your custom testing tool with MCP scale for bigger apps or more complex test scenarios, she highlighted a few practical strategies. One big thing is modular context injection, basically, making sure your tool only deals with the context it really needs at that moment. That keeps things lighter and more efficient.
She also emphasized parallel execution, which makes a huge difference when you’re running lots of tests at once. And if your tool tends to ask the same questions repeatedly, caching those frequent queries can save a ton of time. Monitoring load is another tip, keeping an eye on how your sidekick behaves under heavy usage helps you catch bottlenecks before they become a problem.
Finally, one of her key recommendations was to keep your sidekicks stateless, or at least minimally stateful. That way, they don’t get bogged down by managing too much internal data, which makes them much easier to scale across larger applications. Honestly, these tips make the whole process feel way more manageable and practical than just thinking in theory.
Ah, I totally get where you’re coming from with this question, I was actually at Viktoria Semaan’s session on this at Testmu 2025, and it really clicked for me. Here’s how I think about it: rather than going all-in on one approach, a mix usually works best.
Think of it like this: keep a solid library of your common UI flows, those everyday scenarios that barely change. That gives you a stable foundation you can rely on. But then, for the trickier or constantly changing parts, let MCP step in and generate or tweak tests on the fly. It’s like having a sidekick that’s ready to help whenever something new pops up.
This way, you’re not stuck manually updating everything, but you also don’t lose control. You get the best of both worlds: reliability from your core library, and flexibility plus creative coverage from MCP. Honestly, that’s the balance I’d go for if I were building a long-term Playwright testing setup.
Oh, I loved this part of Viktoria Semaan’s session at Testmu 2025! So, when it comes to handling tricky, domain-specific failures in your app versus the usual, run-of-the-mill exceptions, the key is to mix approaches.
Think of it like this: keep a solid library of tests for your common flows these are your reliable safety nets. Then, for the really specific, unusual cases, let MCP step in to dynamically generate or tweak tests on the fly.
This way, you’re not constantly retraining the model on every little detail of your codebase, but you’re still getting smart, adaptable test coverage where it matters most. It’s kind of like having a dependable co-pilot for your testing, steady on the basics, but flexible and creative when the road gets bumpy.
Oh, I loved Viktoria’s session! One thing that really stuck with me was how surprisingly approachable building these AI-powered testing assistants is. Honestly, most of the work isn’t in tweaking the AI itself, it’s really about defining the context and the rules your assistant needs to follow. That’s where you spend the bulk of your time.
As for machine learning skills, you don’t need to be a data scientist or anything like that. The AI setup is actually quite straightforward. If you’re comfortable thinking through the test scenarios, writing clear rules, and organizing context, you can get these assistants up and running without diving deep into ML algorithms. It’s more about understanding your testing workflow than mastering complex AI math.
Yeah, I got to see this in action during Viktoria’s session, and it was really cool! With MCP Appium Gestures, the tool doesn’t just stick to the usual accessibility IDs. It can also “read” the screen visually, which means it can locate custom UI elements even if they don’t have standard identifiers.
From my experience, especially on complex or dynamic screens, I always give a quick visual check of the elements the tool identifies. Most of the time it nails it, but that small verification step makes sure nothing slips through. Honestly, it feels like having a smart testing sidekick that mostly knows what it’s doing but still loves a bit of human guidance for the tricky parts!
Think of your AI sidekick like a really curious teammate, it wants to explore all sorts of ideas, some brilliant, some a bit wild. The trick is to give it clear boundaries through your prompts and rules. That way, it can still surprise you with edge cases that you might never think of, but it won’t go off chasing completely unrealistic scenarios.
Then, as humans, we step in to review and validate the tests, making sure everything is relevant and safe. It’s kind of like letting your teammate brainstorm freely but keeping the “safety net” in place so the final work stays solid.
Honestly, the easiest way to get started is to keep it really simple at first. Pick just one module to focus on, don’t try to build the whole sidekick in one go. Make sure you clearly define the context for what that module is supposed to do, and then check its outputs manually.
From my experience watching Viktoria’s session at Testmu 2025, starting this way keeps things from getting overwhelming. You avoid getting tangled in a complicated setup, and at the same time, you build a solid foundation. Once that first module is working well, expanding your testing sidekick becomes way easier and much less stressful.
Oh, I really loved Viktoria’s session on this! So, here’s what I got from it: the Model Context Protocol, or MCP, is like having a smart sidekick for your testing. Basically, you feed it things like the user flows, the current state of your app modules, and info about your testing environment. Then MCP uses all that context to generate Playwright scripts that are aware of exactly what’s happening in your app.
From my experience, this makes testing Electron apps way easier, especially when you’re dealing with multi-step workflows that normally get messy. Instead of manually writing tons of scripts and trying to account for every state, MCP kind of “gets it” and helps you automate smarter. Honestly, it felt like a massive time-saver and reduced a lot of the usual headache when testing complex scenarios.
Absolutely! MCP can definitely help with generating BDD-style test scenarios. Think of it as your assistant that takes user stories and turns them into Given-When-Then format for you. It saves time by handling the initial structure, but here’s the thing, it’s not a complete hands-off process.
You’ll still want to review and fine-tune those scenarios to make sure the context, domain logic, and real-world expectations are accurate. MCP handles the “what,” but you bring in the “why” and “how” that only human testers truly understand. So, it’s a great collaboration MCP speeds up the groundwork, and you make sure it all aligns with how things actually work.
That’s a great question and something Viktoria touched on really well during her session.
When your testing sidekick starts to grow, adding AI features, new browser support, or more integrations, things can get messy fast if you don’t stay intentional about structure. The key is modularity. Keep your MCP-based components small, focused, and loosely coupled. That way, if one piece changes, like a new AI analysis module, it doesn’t break the rest of your setup.
Another big one is documenting your context schemas. It’s easy to lose track of what your sidekick “knows” and how it processes data. Having those schemas clearly defined saves you and anyone else using your tool from confusion later.
And of course, run regular regression checks, especially on your AI-generated tests. Models can drift, prompts can evolve, and small updates can lead to surprising results. Catching that early keeps your system reliable.
As for balancing innovation with stability, think of it like maintaining a car. You can add turbo boosts and new features, but only after checking your engine and brakes. Try out new MCP features in controlled experiments or sandboxed branches, measure their impact, and then merge them into your main workflow once they prove stable.
So yes, experiment boldly but with structure. That’s how you keep your testing sidekick both cutting-edge and dependable.
Absolutely! MCP can definitely help with generating BDD-style test scenarios. Think of it as your assistant that takes user stories and turns them into Given-When-Then format for you. It saves time by handling the initial structure, but here’s the thing, it’s not a complete hands-off process.
You’ll still want to review and fine-tune those scenarios to make sure the context, domain logic, and real-world expectations are accurate. MCP handles the “what,” but you bring in the “why” and “how” that only human testers truly understand. So, it’s a great collaboration MCP speeds up the groundwork, and you make sure it all aligns with how things actually work.
Absolutely! That’s where the real magic of Model Context Protocol (MCP) comes in.
Think of it like this: developers can use MCP to get real-time guidance while writing unit tests, almost like having a testing sidekick that understands context and best practices. It can catch repetitive issues, suggest missing test scenarios, or highlight areas that need better coverage. This helps shift testing left without adding extra work for developers.
When it comes to safety and ethics, that’s where humans stay in the loop. The trick is to set up lightweight review checkpoints, not roadblocks, inside your CI/CD flow. Let AI handle the heavy, repetitive checks so your team can focus on what truly needs human judgment, like validating critical logic or ethical considerations.
In short: AI speeds things up, and humans keep it smart and safe.
That’s a great question.
They showed that when two AI agents test together, one acting as the tester and the other as the system under test, they naturally explore a variety of input sequences that a single tester might overlook. This interaction helps uncover those multi-step failures that only appear after a specific chain of actions, which are often hard to catch with regular automation.
To confirm that these weren’t just random AI findings, they compared the AI-discovered issues with human-defined edge cases and real-world examples. When the results consistently matched real bugs and known problem areas, it validated that agent-to-agent testing can truly surface hidden, multi-step failure modes.
It’s almost like having two smart testers bouncing ideas off each other until they find the weak spots no one else noticed.