Using AI Transformer Models in Software Testing

AI doesn’t replace humans. It gives them leverage. Modern QA teams aren’t short on skill - they’re overloaded with complexity. Test debt keeps growing. Frameworks multiply. CI/CD pipelines get heavier. And maintaining automation often feels harder than building the product itself. That’s why KaneAI by TestMu AI (formerly LambdaTest) stands out. Not because it claims to “replace QA.” But because it’s designed to work with QA teams - inside their real workflows. Here’s what genuinely impressed me: 👉 AI-native test scenario generation - Convert plain text, PDFs, Jira tickets, spreadsheets, images, even audio into structured, executable test cases. 👉 Natural language test authoring - Author simple and complex test cases using natural language, just like talking to your teammate. 👉 Advanced natural-language conditionals - Express complex assertions without wrestling with syntax. 👉 Web + mobile support - Comprehensive coverage without framework lock-in. 👉 Multi-language code export - Integrate directly into your existing toolchain. 👉 Integration-first design - Native flows with Jira, GitHub, and CI/CD pipelines. 👉 Data-driven testing + API coverage - Build smarter, reusable test suites. 👉 Auto bug detection + auto-healing - Reduce breakage from UI changes while keeping human oversight intact. The positioning matters. KaneAI isn’t a black box. It’s an intelligent assistant for quality engineers - helping reduce manual overhead, speed up test authoring, and keep automation resilient as products evolve. Because the real opportunity isn’t “doing testing with AI.” It’s giving QA teams back time to focus on strategy, edge cases, and higher-value validation. If your team reclaimed even 30–50% of test creation time - where would you invest that capacity? Explore more: https://lnkd.in/gaZbPxNt #QualityEngineering #SoftwareTesting #AI #AutomationTesting #SoftwareEngineering

I've been exploring the practical limits of using large context window AI models (like ChatGPT o3, Gemini 2.5) for complex coding tasks. The idea of feeding an entire codebase (tens of thousands of lines) and getting precise bug fixes is appealing, but in my recent experience, it doesn't quite deliver on that specific promise for deeply nuanced issues. It seems these large context windows are currently more effective for understanding the overall structure or answering general questions about the codebase rather than performing intricate debugging autonomously. Learned this after dedicating significant time hoping for an easier fix! This exploration led me to pivot my approach. Vibe coding an initial structure feels like a valuable first step. The testing process seems to benefit from the structure and framework that gets initially built from vibe coding. Currently testing a strategy: using Gemini's large context capacity not to directly fix bugs, but to generate a comprehensive test plan based on the full contextual understanding of the codebase. The idea is then to use that plan to drive development and fixes through systematic testing (TDD). The initial results comparing Gemini's generated plan to ChatGPT o3's were interesting, with Gemini's seeming more actionable for this purpose in my case. It reinforces the idea that while AI is a powerful assistant, it requires specific direction, and rigorous testing remains crucial, especially given that AI-generated code can sometimes introduce its own issues. Sharing this journey in case these observations are helpful to others working with AI in software development. The path forward seems to involve leveraging AI's strengths (like analysis for planning) while acknowledging its current limitations. YouTube Link(Higher Quality): https://lnkd.in/gNp3fTfc #AICoding #SoftwareDevelopment #LLM #LargeContextWindow #GeminiAI #Claude3 #DeveloperTools #Programming #TestDrivenDevelopment #AIinSoftware

🚀 AI Tools Every Software Tester Should Know in 2025 AI is no longer a “nice-to-have” for testers — it’s becoming a daily productivity booster. Here’s a simple breakdown of how testers can use AI tools in real projects, not just theory. ⸻ 🔍 Research & Test Design Tools: ChatGPT, Claude, Perplexity, Gemini ✅ Generate test cases from requirements ✅ Understand complex business logic ✅ Research browser, framework & tool updates faster 📌 Example: Turn a user story into positive & negative test cases in seconds. ⸻ 🤖 Automation Testing Tools: Playwright, mabl, Vibium ✅ Faster execution with auto-wait ✅ Less flaky tests ✅ Low-code automation for quick coverage 📌 Example: Testing dynamic React apps without using Thread.sleep(). ⸻ 🧠 Local AI (Secure & Offline) Tools: Ollama, LangChain, Hugging Face ✅ Run AI locally (no data leakage) ✅ Analyze logs, failures & test data ✅ Classify bugs automatically 📌 Example: AI reads failed test logs and suggests root cause. ⸻ ✍️ Scripting & Coding Assistants Tools: GitHub Copilot, JetBrains AI, Gemini Code Assist ✅ Auto-generate Selenium / Playwright code ✅ Improve locators & wait strategies ✅ Reduce boilerplate coding 📌 Example: Write automation scripts 40–50% faster. ⸻ 👁️ Visual Testing Tools: Applitools, BrowserStack Percy ✅ Detect UI breaks humans miss ✅ Pixel-perfect regression testing ✅ Cross-browser & device validation 📌 Example: Catch layout issues that functional tests won’t detect. ⸻ 🧪 Testing AI Systems Tools: Promptfoo, DeepEval, Ragas, OpenEvals ✅ Validate chatbot responses ✅ Detect hallucinations & incorrect answers ✅ Measure accuracy & relevance 📌 Example: Testing AI support bots like we test APIs. ⸻ 🎯 Key Takeaway AI won’t replace testers. 👉 Testers who use AI will replace those who don’t. Start small. Pick one tool. Integrate it into your daily testing workflow. #SoftwareTesting #AutomationTesting #AITesting #Playwright #Selenium #QualityEngineering #TestAutomation #AIForTesters #QA #TechCareers

In modern software development, writing code is only half the job — testing it is just as critical. But as codebases grow, maintaining strong unit test coverage becomes increasingly challenging. A recent engineering blog from The New York Times explores an interesting approach: using generative AI tools to help scale unit test creation across a large frontend codebase. - The team built an AI-assisted workflow that systematically identifies gaps in test coverage and generates unit tests to fill them. Using a custom coverage analysis tool and carefully designed prompts, the AI proposes new test cases while following strict guardrails — such as never modifying the underlying source code. Engineers then review and refine the generated tests before merging them. - This human-in-the-loop approach proved surprisingly effective. In several projects, test coverage increased from the low double digits to around 80%, while the time engineers spent writing repetitive test scaffolding dropped significantly. The process also follows a simple iterative loop: measure coverage, generate tests, validate results, and repeat. The experiment also highlighted some limitations. AI can hallucinate tests, lose context in large codebases, or produce outputs that require careful review. The takeaway: AI works best as an accelerator — not a replacement — for engineering judgment. As these tools mature, this kind of collaborative workflow may become a practical way for teams to scale reliability without slowing down development. #DataScience #MachineLearning #SoftwareEngineering #AIinEngineering #GenerativeAI #DeveloperProductivity #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gj9fc322