Cross-Device Testing Strategies

Measuring AI Assistants Across Devices: The CRAB Benchmark ... As AI assistants become increasingly sophisticated, a critical question emerges: How do we effectively evaluate their ability to handle complex, multi-device tasks that mirror real-world scenarios? A new research paper introduces CRAB (Cross-environment Agent Benchmark), an innovative framework designed to address this challenge. 👉 Why CRAB Matters In our interconnected digital world, we frequently use multiple devices to complete tasks. For example, you might start drafting an email on your phone and finish it on your laptop. CRAB is the first benchmark that allows researchers to assess AI agents' performance across different platforms, providing a more realistic evaluation of their capabilities. 👉 Key Innovations 1. Cross-Environment Testing: CRAB enables the evaluation of AI agents across multiple device environments, such as desktops and mobile phones. This approach allows for testing more complex, realistic tasks that span different platforms. 2. Graph-based Evaluation: The novel "Graph Evaluator" method breaks tasks into sub-goals, offering more nuanced performance metrics beyond simple success or failure. This approach accommodates multiple valid solution paths, reflecting the diversity of real-world problem-solving. 3. Efficient Task Construction: CRAB introduces a sub-task composition method for creating complex cross-platform tasks and a graph-based approach for streamlining evaluator creation. This efficiency is crucial for developing comprehensive benchmarks. 4. Comprehensive Dataset: The initial CRAB benchmark includes 100 tasks across desktop and mobile environments, with varying difficulty levels to test different agent capabilities. 👉 Results and Implications The researchers evaluated several leading AI models, including GPT-4, Claude 3, and Gemini 1.5, in both single and multi-agent configurations. The highest-performing setup was a single GPT-4 agent, achieving a 35.26% task completion rate. These results highlight several important points: 1. There's significant room for improvement in cross-platform AI capabilities. 2. The performance gap between different models and configurations provides valuable insights for AI system design. 3. The benchmark reveals the current limitations of even the most advanced AI models in handling complex, multi-device tasks. 👉 Link to the paper in the first comment 👇

A common mistake I see brands make is relying on their own inboxes to test email campaigns. But just because it looks great on your device doesn’t mean it will for your customers. What's often not taken into consideration is how your campaigns render across the 60+ platforms and devices your customers might be viewing your campaigns on. This means that while you and even your team might see a beautifully designed, well-put-together campaign, your customers might be seeing a completely skewed design. Not quite the outcome you'd like... And without proper testing, that beautifully designed campaign could appear distorted, unreadable, or even completely broken for some recipients. Dark mode is a perfect example. It's estimated that around 40% of users have dark mode enabled on their devices, yet most brands don’t test how their emails render in dark mode. The result? Logos that disappear, unreadable text, and broken design elements that ruin the user experience. Internally, we use Litmus to check formatting, links, and deliverability before sending and while this is our go-to, Sinch Email on Acid also does the trick and is much more cost-effective for brands. To give you an idea, here's what you can do using a third-party tool like Litmus or Emails on Acid: ✔️ Ensure emails display correctly, including in dark mode ✔️ Make sure all links work ✔️ Confirm compatibility across 60+ devices ✔️ Prevent email clipping, especially in Gmail (102KB limit) ✔️ Minimise human error by testing beyond just your inbox ✔️ Validate mobile responsiveness ✔️ Provide proper authentication to avoid being flagged as spam ✔️ Monitor for blocklists and spam placements ✔️ Check email load times to avoid slow rendering ✔️ Review accessibility compliance (contrast, font size, readability) I’m still waiting for an ESP to integrate this functionality directly - it would be a game changer. Until then, proper testing is non-negotiable.

With new mobile devices constantly entering the market, ensuring compatibility is more challenging than ever. Compatibility issues can lead to poor user experiences, frustrating users with crashes and functionality problems. Staying ahead with comprehensive testing across a wide range of devices is crucial for maintaining user satisfaction and app reliability. I would like to share the strategy that I have used for comparability testing of mobile applications. 1️⃣ Early Sprint Testing: Emulators During the early stages of development within a sprint, leverage emulators. They are cost-effective and allow for rapid testing, ensuring you catch critical bugs early. 2️⃣ Stabilization Phase: Physical Devices As your application begins to stabilize, transition to testing on physical devices. This shift helps identify real-world issues related to device-specific behaviors, network conditions, and more. 3️⃣ Hardening/Release Sprint: Cloud-Based Devices In the final stages, particularly during the hardening or release sprint, use cloud-based device farms. This approach ensures your app is tested across a wide array of devices and configurations, catching any last-minute issues that could impact user experience. Adopting this 3 tiered approach ensures a comprehensive testing coverage, leading to a more reliable and user-friendly application. What is the strategy that you are adopting for testing your mobile apps. Please share your views as comments. #MobileTesting #SoftwareTesting #QualityAssurance #Testmetry