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After attending Oracle AI World, one thing is clear: Oracle is all in on AI. From embedded agents across Fusion Applications to the AI-native Oracle Database and OCI’s massive model-training capabilities, Oracle is positioning itself as the platform where enterprise AI comes to life.The energy was infectious. Every session, demo, and conversation carried a sense of acceleration: faster decisions, faster workflows, faster innovation. But beneath the excitement, one question kept coming up in my mind (and in quiet side conversations with customers and prospects):How do we move fast with AI without breaking what’s already working?That’s where Tricentis comes in. (Learn how in our webinar, Future-proof your Oracle automation.)

Functional testers of Oracle applications – whether Oracle Fusion Cloud or E-Business Suite – often face tight timelines and frequent UI changes. This week’s tip shows a practical way to use Tricentis Tosca’s AI-powered capabilities to accelerate and strengthen your functional testing of Oracle apps. We’ll focus on Vision AI, Tosca’s AI engine, and how it lets you create and run tests earlier and more resiliently. Let’s dive in!Test Oracle Updates Early Using Vision AIOracle Fusion updates roll out quarterly, and you typically get only a short window (about two weeks) to test changes in a staging environment. But did you know you can start building your automated tests weeks earlier? With Tosca’s Vision AI, you can create test cases from Oracle’s UI prototypes (the previews in Oracle’s Upgrade Readiness documentation) before the act

Exploratory testing can feel like a software adventure – you wander through an app, poke at features, and see what surprises pop up. This week’s tip shows how AI can be your friendly sidekick on that adventure. We’ll explain what exploratory testing is, then explore how AI tools (like ChatGPT) can help you generate clever test ideas, write test charters, and even document your findings. Let’s dive in!What is Exploratory Testing?Exploratory testing is a flexible, dynamic approach to software testing. Instead of following a strict script of predefined test cases, you explore the software in real time, learning and adjusting as you go. Testers use their intuition and creativity to try various flows and inputs, identifying bugs that scripted tests might miss. In essence, it’s the art of simultaneously designing and executing tests on the fly, which makes it great for uncovering hidden is

The Challenge of Salesforce TestingSalesforce is a powerful but complex platform, and testing it can sometimes feel like finding your way through a labyrinth. QA engineers often face tedious, repetitive test scenarios that must cover countless combinations of objects, fields, and user permissions. Manually clicking through each scenario (or writing long Selenium scripts) isn’t just time-consuming; it’s also prone to human error and difficult to maintain. Every time Salesforce updates or a business process changes, testers may spend hours updating scripts or re-running manual tests to ensure nothing broke.Sound familiar? If you’ve ever had to verify every picklist value for every user role, or retest a 100-step flow after a minor change, you know the pain. Even traditional automation can require a whole team of experienced coders building and fixing scripts. It’s enough to make any tester reach for a

Quality Assurance teams can supercharge their testing by using AI to generate synthetic test data. Why? AI-driven data generation creates realistic yet fake data that mimics real user behavior; all without exposing any actual user info. This means you get edge cases and diverse scenarios covered with full privacy compliance. Below is a step-by-step guide to leveraging AI for smarter test data.Why Use AI for Synthetic Test Data? Uncover Edge Cases: AI can produce data variations humans might miss, surfacing rare or tricky inputs (e.g. very long names, special characters, unusual date formats) that help you catch bugs early. In fact, next-gen AI can mirror production scenarios – including boundary conditions and odd cases humans wouldn’t even consider. Privacy-Sensitive Testing: With regulations like GDPR/CCPA, using re

In today’s DevOps-driven world, QA teams are expected not only to test for functionality but also for performance under real-world conditions. How can we ensure our performance tests reflect what happens in production? One powerful approach is to integrate Application Performance Monitoring (APM) tools like DataDog directly into performance testing workflows. This article explores why this integration is so important and how it works in practice – in particular, how Tricentis NeoLoad (a performance testing tool) works hand-in-hand with DataDog’s APM. We’ll break down the technical details (tagging virtual users, data flows, API keys) in an accessible way and highlight the big benefits for QA testers and developers. Let’s dive in!Why Combine APM with Performance Testing?Performance testing and APM are a natural fit. APM tools such as DataDog are traditionally used by IT Operations to monitor apps in p

The Challenge of Frequent Oracle UpdatesOracle ERP systems – whether on-premises E-Business Suite (EBS) or cloud-based Fusion Applications – receive frequent updates (Oracle Fusion, for example, ships new features quarterly). For QA engineers, each update can mean an overwhelming regression test cycle to ensure nothing breaks. Testing everything is slow and resource-intensive, but testing only a guesswork subset risks missing critical bugs. How can teams speed up testing without sacrificing reliability? This is where AI-driven change impact analysis and intelligent test selection come in.AI to the Rescue: Change Impact Analysis for Smarter TestingChange impact analysis uses AI and analytics to pinpoint what parts of the system are affected by a change, and thus what needs testing. Instead of relying on intuition, the AI combs through code changes, configurations, and even usage logs to identify impacted modules and business

Hello ShiftSync community!Welcome back to our biweekly AI Tip of the Week series, where we explore practical ways to integrate AI into testing workflows. In Agile environments, code changes roll in quickly – and rerunning every test case after each change can be overkill. This week’s tip is about using AI to prioritize test cases based on risk and recent changes. In short, we’ll leverage AI-driven analysis to automatically figure out which tests are most valuable to run when code changes, so you catch critical bugs faster with less effort.Illustration: AI-driven test prioritization focuses your regression suite on the riskiest, most impacted areas after each code update. Instead of running an entire test suite blindly, AI algorithms analyze what changed (and past failures) to select the high-impact tests that should be run first. This targeted approach saves time and finds defects sooner.How AI Helps Priorit