Technical Discussion

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

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

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

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

Before we start, I would like to let you know that a challenge will be coming soon and will be posted here on ShiftSync, where you can participate in it for a chance to win a ShiftSync Giftbox. So stay tuned for that and drop a comment down below if you want to be a frontrunner in the challenge. Now, without further ado, let’s get on with the article. The tech industry has been talking about the applications of AI in testing for the last two decades. The rise of Generative AI brings AI closer to everyday testing tasks and challenges. We stand at the gateway into the future of software testing, a world where AI is a powerful assistant, instead of merely being a buzzword. As an active participant in the testing world, I can foresee AI will transform testing workflows, creating room for creative, efficient, and innovative approaches. In this article, let’s uncover different ways in which AI can be incorporated into testing, both creatively and precisely.  Learning The way we learn, and th

Below is a recap of our webinar with @dave Colwell, the Artificial Intelligence & Machine Learning VP at Tricentis. You can view the original in the link below, just be sure that you’re logged in 🙂  Is Generative AI taking over testing? David Colwell webinar recapThe 3 main questions Why do we need AI? How can we use AI?  How do we test AI?  1. Why do we need AI? Before we begin, we need to address the idea of the human attention span. Take a moment to consider these facts about attention across various demographics:  A human’s attention span, put simply, is not good. It gets even worse when you start putting multiple screens in front of someone, a common scenario for many of us today. We’ve found that the average person can hold roughly 7 pieces of information in their working memory at a time, give or take. With apps like TikTok and other distraction-heavy apps on the rise, this stat doesn’t seem like it’s going to improve any time soon. So how does this relate to AI? Consider w

In today's world, whether you're on social media or diving into the tech universe, one term that resonates consistently is AI. It is not just technology; it's the art of empowering machines to think, learn, and innovate—ushering in a new era of boundless possibilities. In every domain, improving quality is crucial, and to achieve this, we conduct end-to-end testing, addressing all aspects of the testing cycle.  In this post, we'll explore how AI can assist us in testing, enhancing product quality with smart strategies. But before we plunge into the exciting exploration of Quality Assurance and analyse how AI transforms End-to-End Testing.Basics of AI, ML, and Generative AIBefore we dive into discussing the use of AI in testing, let's explore some complex terms that form the basis of AI. People often mix up terms like AI, ML, Generative AI, Code Generation, and LLMs, causing confusion. So, let's take a moment to clear up this confusion and untangle these concepts.Each term has its own s

This article is an overview of the Hexagonal Architecture webinar with @alistair_cockburn. You can view the webinar in its entirety here. Hexagonal ArchitectureHexagonal Architecture, also known as Ports and Adapters, is a design pattern that plays a pivotal role in modern software development. Its primary purpose is to create flexible and maintainable software systems by separating the core application logic from external interfaces and concerns. In this article, we will delve into the concepts and ideas discussed in a lecture on Hexagonal Architecture and explore its benefits, costs, and real-world applications. Introduction to Hexagonal ArchitectureAt its core, Hexagonal Architecture is about creating a clear boundary between the essential logic of an application and its interactions with external systems and interfaces. This separation allows developers to design software that is highly adaptable, testable, and resistant to change, making it an invaluable tool in the world of softw

By Larry Loeb, Veteran Technology Editor and AuthorBehold, the five best practices that can help maximize advantages while assisting with load testing in an Agile environment.1. Make performance SLAs a focus areaPerformance needs to be somewhere on the task board if the team is going to give it attention. Otherwise, it will get ignored. One effective way to ensure inclusion of performance is using your performance service level agreements (SLAs) as acceptance testing for each story. That means the story cannot be “done” if changes will cause the application to fall short of the SLAs.This discipline works well if the changes made to the story will affect a relatively small section of the overall code. The performance issues would, therefore, be confined to a portion of the application.For SLAs that are general across the entire application, tests should be added to a more extensive list of constraints (which may include functional tests) that will be tested for every story to determine