Technical Discussion

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 issues in ways formal testing might not.Think of exploratory testing like freestyle jazz: you have a gener

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 third cup of coffee! ☕️But what if testing Salesforce could be smarter, faster, and (da

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 real customer data in tests is a no-go. Synthetic data generated by AI is fully anonymous and exempt from data protection rules. You can safely test workflows involving pe

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 production – tracking metrics like CPU usage, memory, database response times, etc., to

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 processes. This lets QA focus on the at-risk areas and skip te

Flaky testsThose that pass sometimes and fail other times with no code changes, are a recurring headache in test automation. They waste time, erode trust in the test suite, and slow down release cycles. The good news is that AI can help identify, diagnose, and reduce these pesky false failures. Below is a grounded, step-by-step workflow for QA teams to apply AI in real test environments to handle flaky tests. This isn’t theory, it’s a practical approach you can use regardless of which tools you have (though we’ll note a couple of examples like Tricentis where relevant).Step-by-Step Workflow: Reducing Flaky Tests with AI 1. Detect Flaky Patterns with AI:Start by aggregating your test results over time (from CI pipelines or test management). Use AI/ML analytics to spot patterns of inconsistency – for example, tests that often fail intermittently across runs. AI-powered tools can analyze past execution data (timestamps, environments, error logs) to flag tests that are non-deterministic. T

Modern QA and DevOps teams are increasingly shifting performance testing to the left – executing load tests earlier in the development cycle to catch issues sooner. Integrating Tricentis NeoLoad into continuous integration (CI) pipelines makes this possible by automating performance tests as part of your build and deployment process. In this article, we’ll explore how to use NeoLoad with CI pipelines (as highlighted in the Tricentis Expert Session webinar on this topic) and cover key insights from the webinar’s technical demo. We will discuss why early performance testing is important, how the NeoLoad CLI enables CI integration, a recap of executing NeoLoad tests via command line and Jenkins, GitOps-style management of test assets in source control, automating test analysis/reporting, and best practices to achieve scalable, continuous performance testing in your pipelines.Why Shift-Left Performance Testing MattersTraditionally, performance testing was done late in the software lifecycl

QA teams often focus on predefined test cases and code coverage, but real user behavior in production can reveal gaps in your testing. By leveraging AI to analyze production logs, clickstreams, and session data, you can identify which user journeys or edge cases aren’t covered by your QA tests. This ensures your test suite mirrors how people actually use the product, catching issues that slip past traditional test design.Why Look at Production User Behavior?In production, users may take paths and encounter scenarios that QA never anticipated. Analyzing this real-world usage uncovers high-traffic features and unusual workflows. These insights help pinpoint “blind spots” in your current test coverage – areas of the application that users engage with frequently or in unexpected ways, but which your tests don’t exercise. AI tools excel at sifting through large volumes of user interaction data (logs, analytics) to spot patterns and outliers that manual analysis might miss. This means common

In the ever-evolving world of software testing, AI is opening up transformative possibilities for QA teams. Tricentis has leveraged its years of AI innovation (from Vision AI to self-healing tests) to introduce Agentic Test Automation – an AI-powered “co-worker” embedded in Tricentis Tosca that can autonomously generate and execute test cases from natural language descriptions. This advanced capability is poised to revolutionize how QA engineers and software testers design automated tests, addressing modern challenges of scale and complexity in software delivery.Modern Testing Challenges and the Need for AI AssistanceDevelopment cycles are accelerating and codebases are growing larger than ever. With the advent of generative coding tools, developers can produce code at unprecedented speed – but this leaves QA teams scrambling to test more functionality in less time. Moreover, AI-generated code can introduce unexpected issues and variations, demanding smarter testing strategies. Traditi

OverviewTricentis Tosca Cloud now supports the Model Context Protocol (MCP) integration, allowing QA engineers to use AI assistants (like Cursor AI or Visual Studio Code) to interact with Tosca Cloud via natural language. In practical terms, MCP lets you perform Tosca Cloud testing tasks directly from your development environment – no more switching between tools. You can automate test case creation, analyze failures, and even simulate APIs right from your IDE. Below, we explain what MCP enables, how to configure it securely, and the new workflows it unlocks.What MCP Enables in Tosca CloudMCP is essentially a bridge between Tosca Cloud and your AI-powered development tools. Here’s what it makes possible for your testing workflow: AI-driven test design: Automate tedious test case creation by letting the AI assistant scaffold test cases for your application flows. This accelerates test design and ensures you cover key user scenarios. Smart failure analysis: Gain insights into failed te