InfoQ Homepage Test Automation Content on InfoQ
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Vitest 4.1: Test Tags, Native Node.js Execution and AI Agent Reporter
Vitest 4.1, developed by VoidZero, enhances JavaScript testing with features like test tags for filtering and configuring tests, an experimental mode to bypass Vite's module runner, and new lifecycle hooks. It supports Vite 8 from the start. Notably, it reports improvements in performance compared to Jest. The release addresses issues and provides guides for migration.
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Sauce Labs Launches AI Agent to Automate Test Creation and Close the DevOps “Velocity Gap”
Sauce Labs has announced the general availability of Sauce AI for Test Authoring, an AI-driven agent designed to translate business intent directly into executable test suites, marking a shift toward what the company calls Intent-Driven Testing.
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Uber Migrates 75,000+ Test Classes from Junit 4 to Junit 5 Using Automated Code Transformation
Uber engineers migrated over 75,000 test classes from JUnit 4 to JUnit 5 using automated code transformation with OpenRewrite and internal orchestration. By enabling the JUnit Platform for dual execution with Bazel and validating changes through CI, the team modernized testing infrastructure while maintaining correctness at monorepo scale.
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Meta Reports 4x Higher Bug Detection with Just-in-Time Testing
Meta introduces Just-in-Time (JiT) testing, a dynamic approach that generates tests during code review instead of relying on static test suites. The system improves bug detection by ~4x in AI-assisted development using LLMs, mutation testing, and intent-aware workflows like Dodgy Diff. It reflects a shift toward change-aware, AI-driven software testing in agentic development environments.
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DoorDash Builds LLM Conversation Simulator to Test Customer Support Chatbots at Scale
DoorDash engineers built a simulation and evaluation flywheel to test large language model customer support chatbots at scale. The system generates multi-turn synthetic conversations using historical transcripts and backend mocks, evaluates outcomes with an LLM-as-judge framework, and enables rapid iteration on prompts, context, and system design before production deployment.
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How a Small Enablement Team Supported Adopting a Single Environment for Distributed Testing
Po Linn Chia presented how they re-used a single development environment to deploy multiple service versions for testing their distributed system in her presentation "No QA Environment? No Problem" at Dev Summit Boston. A small enablement team, cultural buy-in, and gradual learning helped teams collaborate, reduce cognitive load, and scale testing practices.
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Leapwork Research Shows Why AI in Testing Still Depends on Reliability, Not Just Innovation
Leapwork recently released new research showing that while confidence in AI-driven software testing is growing rapidly, accuracy, stability, and ongoing manual effort remain decisive factors in how far teams are willing to trust automation.
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How AI with Prompt Engineering Supports Software Testing
AI is becoming a key QA tool, aiding in faster scenario generation, risk detection, and test planning. Arbaz Surti showed how effective prompting using roles, context, and output format helps to get clear, relevant, and actionable test scenarios. AI can boost testers, but human judgment is needed to ensure relevance and quality.
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Changing a Career from Developing Software to Test Automation
A developer who became a test automation engineer faced a challenging learning curve due to limited testing experience. He learned the importance of test levels, when not to automate, and how QA is vital to quality. Motivated by impact, growth, and teamwork, he values communication and continuous learning.
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How to Enable Testing a Distributed System on a Single Environment Using Proxy Routing
Without a dedicated QA environment, teams faced tech and coordination issues when testing a distributed system. A slow, unmaintainable CLI led an organization to shift left with automated testing. They built a tool for versioned deployments using CI and proxy routing, enabling developers to run isolated tests on multiple versions to catch bugs earlier.
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Microsoft Launches Azure App Testing: a Unified Hub for Load and End-to-End Testing
Introducing Azure App Testing: a unified hub combining Azure Load Testing and Microsoft Playwright for streamlined, efficient application testing. With AI-powered tools for accelerated performance insights and seamless scaling, users can simulate real-world traffic across multiple regions. Optimize your testing experience and ensure top-notch app performance with Azure's innovative solutions.
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How to Use Property-Based Testing as Fuzzy Unit Testing
According to Eivind Jahren, property-based testing is an invaluable tool for its ease of use and effectiveness. It is flexible in what requirements one can formulate and is simple and lightweight enough to put in the hands of software developers to perform iterative testing on a daily basis, less test code is required, and it’s easier to reuse test data generators for complex structured data.
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JetBrains Aqua IDE for Test Automation Now Generally Available
Aqua, the first IDE for test automation, is now generally available. The IDE supports multiple languages and major testing frameworks like Selenium and Cypress. JetBrains introduces a new licensing model with Free Individual Non-Commercial and Paid Commercial plans. Additionally, Aqua is included in the All Products Pack.
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Slack Combines ASTs with Large Language Models to Automatically Convert 80% of 15,000 Unit Tests
Slack's engineering team recently published how it used a large language model (LLM) to automatically convert 15,000 unit and integration tests from Enzyme to React Testing Library (RTL). By combining Abstract Syntax Tree (AST) transformations and AI-powered automation, Slack's innovative approach resulted in an 80% conversion success rate, significantly reducing the manual effort required.
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Booking.com Doubles Delivery Performance Using DORA Metrics and Micro Frontends
The team in Booking.com’s fintech business unit implemented a series of improvements across the backend and the frontend of its platform and was able to double the delivery performance, as measured by DORA metrics. Additionally, the Micro Frontends (MFE) pattern was used to break up the monolithic FE application into multiple decomposed apps that could be deployed separately.