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Hello, UltimateQA enthusiasts!

Automation has always been about efficiency — but 2025 is the year it becomes about intelligence.

The testers who thrive now aren’t just writing scripts; they’re designing systems that learn, adapt, and predict. As AI reshapes how we build and test software, the question is no longer “Can we automate this?” — it’s “What should humans still own?”

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Failures happen. Temporal makes them irrelevant. Build applications that never lose state, even when everything else fails.

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🎥 Video of the Week

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Nikolay Advolodkin "When you first run into build or runtime errors (which is likely on diverse developer machines), copy error messages into Copilot Chat and put the agent into agent mode to have it suggest or even run remediation steps. Use high-quality models for software tasks (GPT-5 or Claude Sonnet 4+, where available) to reduce hallucination and iteration cycles."

👩‍💻 Useful Libraries

🗞️ Article of the Week

GitHub Spark – Microsoft’s Latest AI Developer In this article, we’ll explore how GitHub Spark works, its standout features, and how it compares to other popular AI-powered development platforms, such as Replit, V0, and Lovable. READ NOW

💡 Quick Tips & Tricks

• Retry + Fallback Locators: Implement multiple locator strategies (CSS, XPath, attributes, partial text). If one fails, your framework automatically retries the next option. This reduces false negatives caused by minor UI changes.
• Conditional Dynamic Waits: Replace fixed sleeps with conditional waits that poll until an element is visible, stable, and ready for interaction. Example: check visibility and ensure no DOM movement for the last 200ms. This eliminates most timing issues.
• AI-Powered Self-Healing Locators: Adopt frameworks that use AI-driven healing to automatically adapt locators when the UI changes (e.g., a button is moved or an ID is renamed). These tools compare DOM structures and attributes to reduce flaky tests with minimal human intervention.
• AI-Generated Test Data Variations: Use generative AI to create test datasets with edge cases, boundary values, and real-world combinations directly from requirements or user stories. This enhances coverage and uncovers bugs missed by static datasets.
• Modular Test Assets and Shared Fixtures: Centralize reusable helpers, mocks, and setup/teardown logic in version-controlled modules. This avoids duplication, simplifies maintenance, and ensures consistency across test suites and teams.

ChatGPT Prompt for Test Automation Harness the power of AI to become a strategic tester, not just a script executor. Use this prompt to get ChatGPT to act as your Automation Architect & QA Coach, guiding you from planning to optimization: Prompt: ​ You are an expert Test Automation Architect and AI-assisted QA mentor. I’m working on improving our automated testing process for a [web/mobile/API] application built with [framework/tool]. Please help me: Analyze our current test strategy and identify weak points (coverage, flakiness, CI/CD integration, or data setup). Recommend how AI or machine learning can enhance our pipeline, such as through AI-based test case generation, self-healing scripts, defect prediction, or intelligent prioritization. Suggest improvements to speed up execution and reduce maintenance effort. Provide practical implementation steps and code examples when relevant.

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