Technical Discussion

 Are you ready to supercharge your coding journey? @IOan brought a great opportunity.  From Ioan:  I am excited to share a fantastic opportunity for the Shiftsync community to enhance their skills and master the art of GitHub through a series of FREE courses.These courses are designed to empower you with the knowledge and expertise needed to make the most out of GitHub for your projects.  Unlock the Power of GitHub with These Free Courses:1. GitHub Essentials for Beginners:Dive into the basics of GitHub with this beginner-friendly course. Learn how to create repositories, manage branches, and collaborate effectively with other developers. Perfect for those new to version control or looking to solidify their understanding.2. Mastering Pull Requests and Code Reviews:Pull requests are at the heart of collaboration on GitHub. This course will guide you through the ins and outs of creating pull requests, handling code reviews, and ensuring your contributions align with best practices. Take

Hey ShiftSyncers,Quick one: Anyone doing Advent of Code by Eric Wastl? For those who might be unfamiliar, Advent of Code is an annual coding event that involves solving a series of programming puzzles throughout December, each becoming progressively more challenging. For those already diving into the Advent of Code, I wish you the best of luck! Feel free to keep us posted on your progress, share interesting puzzles, or ask for help if needed. 

When faced with testing large, complex tickets at the last minute, it’s easy to miss important edge cases, which can lead to undetected issues. How do you make sure you thoroughly cover all possible scenarios, including edge cases, given the limited time? What strategies or tools do you use to track these cases, and how do you prioritize the most critical ones to ensure comprehensive testing under pressure?

I’m currently facing significant challenges with our e-delivery process that seem to impact the reliability and speed of our software releases. Despite thorough testing and successful build completions, we often encounter issues during deployment that lead to delays and frustration among team members and stakeholders.Here are some specific challenges we're experiencing:Inconsistent Build Artifacts: Different environments sometimes deploy artifacts that don't match what was tested, causing discrepancies in behavior. Communication Gaps: There seems to be a disconnect between the development and QA teams regarding the status and readiness of builds for delivery. Deployment Failures: We often see unexpected failures during e-delivery that weren't caught during testing, which leads to rollbacks and increased downtime. Dependency Management: Handling external dependencies (like APIs and libraries) can be tricky, and changes in those can break our deployments unexpectedly.I would love to hear

We recently deployed a feature that passed all tests in our Acceptance environment (functional tests, regression, smoke tests), but after deployment to Production, we encountered issues such as broken functionality, unexpected errors, and performance slowdowns.Here’s the setup:Acceptance and Production environments are as close to identical as possible (same configurations, database versions, and server setups). Automated and manual tests ran successfully in Acceptance. The feature in question interacts with both third-party APIs and a legacy system.However, after deployment to Production, users are facing:API timeouts and failed calls to third-party services. Slower performance in certain areas of the application. Data inconsistencies when interacting with the legacy system.Has anyone experienced a similar issue where everything works fine in Acceptance, but things fall apart in Production? What are some potential causes or overlooked areas that could explain this discrepancy?I’m part

What conferences are you attending this Year?Tell the community so that we may see each other there.

1️⃣ Automated Testing - Let the machines do the work, but at what cost?2️⃣ Manual Testing - Human touch is invaluable, or is it?3️⃣ CI/CD Pipeline Testing - Seamless integration or overkill?4️⃣ Exploratory Testing - Creative freedom or chaotic mess?5️⃣ Test-Driven Development (TDD) - Code-first or test-first?🎯 Vote and defend your choice in the comments!What would you sacrifice, and why? Let the debate begin!

Functionality failures in cases where no apparent code or infrastructure changes were made can be tricky to diagnose. Common root causes include environmental drift, where configurations or dependencies in the environment subtly change over time, leading to inconsistencies. Data-related issues, such as corrupted or outdated data, can also break functionality without any direct code changes. External factors like third-party APIs, services, or network-related issues may alter their behavior, impacting the application. Timing or concurrency issues can surface unexpectedly, especially in high-load scenarios. Finally, hidden bugs or edge cases may not have been caught during testing but reveal themselves later due to changing conditions.

Since we are testers we tend to spot pattern and issues even when we do not want to :smiley: .So i was wondering how do you spot a text that is generated by an LLM/AI.Please do not say I use a scanner.For me the tells are usually these 3* Using the prashe "In the world of"* Using words that one does not usually use in a day to day conversation like delve* The post is marked with emojis all the way.What about you?

Automated testing is crucial for fast feedback, but there’s often a trade-off between speed and thorough test coverage. What strategies do you use to ensure that your automated tests deliver rapid feedback without sacrificing coverage? Share your approaches for balancing test execution time with maintaining comprehensive testing, especially in continuous integration environments.

AI and machine learning are transforming many areas of software development, including test automation. Has anyone here integrated AI or ML into their test automation process? How has it affected the effectiveness, speed, and accuracy of your tests? Share your experiences, including any benefits or challenges you've encountered along the way.

In fast-paced DevOps environments, frequent code changes can make thorough testing a challenge. What’s your approach to ensuring high-quality testing without slowing down the development pipeline? How do you balance speed with accuracy, and what strategies or tools do you use to maintain test coverage and effectiveness in such a dynamic setting?

Flaky tests, which produce inconsistent results, can disrupt the development process and reduce confidence in test automation. What is your approach to identifying and managing flaky tests? Do you have any proven strategies or best practices for making them more stable and reliable in your test suite? We'd love to hear your methods for addressing this common challenge.

In scenarios where your application integrates with real-time data feeds (e.g., stock prices, sensor data), how do you approach automated testing? What tools or frameworks help simulate or validate such real-time interactions?

When running automated tests in parallel, what are the most common challenges you've faced, and how did you overcome issues such as test data dependency or browser state? Do you have any strategies for efficiently managing resources?

AI models often operate as black boxes, making their decisions opaque. How do testers ensure they understand and trust AI-driven test results?

Flaky tests can undermine confidence in automated testing. How do testers identify the root causes and resolve these issues?

Exploratory testing is crucial for uncovering unexpected issues, but it can be hard to plan in a time-boxed environment like Agile. How do teams incorporate it effectively?

In a rapidly evolving tech landscape, automation strategies and tools must be adaptable to stay relevant. This question seeks to explore how QA professionals future-proof their automation efforts against emerging technologies and shifts in testing methodologies. It addresses the importance of building flexible frameworks, choosing scalable tools, and staying informed about industry trends. What proactive steps do you take to ensure that your automation processes and tools can seamlessly adapt to future changes, and how do you integrate forward-thinking practices into your overall testing strategy?

Where does return render( <UserProvider> etc go in a test.js jest file? Here is the UserContext.js mock data file```import React, { useState } from 'react';// Create a mock UserContext with default userId as nullconst UserContext = React.createContext({  userId: null,  setUserId: () => {}, // Mock function for tests});// Mock UserProvider to be used in testsconst UserProvider = ({ value, children }) => {  const [userId, setUserId] = useState(value?.userId || null);  // Always use the provided setUserId if available, otherwise use the internal state  const contextValue = {    userId,    setUserId: value?.setUserId || setUserId,  };  return (    <UserContext.Provider value={contextValue}>      {children}    </UserContext.Provider>  );};export { UserContext, UserProvider };``` So does the below go above or in the describes if above do I pass the renderComponentWithUserContext to the describe condition as a parameter and each test that uses it? Apologies for the tro

I want to share how I use a database for storing results of my database comparison which makes them easy filterable.I started with reports in files (and still have them for sharing and long-term storage), each run typically covering multiple subtopics and creating by that multiple files. And that twice for the new and the old database.Going through each file and looking for the same patterns again and again became laborious. Especial because filtering by fatality was demanding (I marked error classes by different colors while going through the files) . Even more when I wanted to see if similar failures happend over multiple subtopics.Now I can filter and sort for anything I want. By regexes!I also show them to others to discuss my findings.If you haven’t done something similar: Feel inspired to do something similar for yourself! Any question?If you have done something similar: How do you do and use it?This reports seldom last longer than some days. When I changed the report tables (esp

Hi Community,I have RTB libaries in a deeper structure. The RTBs have business parameters.When I pass down business parameters,to deeper levels the parameters arrive as NULL in some test plans.D you know whats amiss here?For example:I have a parameter “Role” and want to pass it on to a sub-RTB like this:Scenario A:I pass the parameter down as {PL[Role]}Issue: The parameter arrives as a NULL value Scenario B:I pass the parameter down as {PL[Role]}. On level 2 (RTB 2) I copy the parameter to a buffer variable and pass on the value as {B[Role]}This works.Regards    Florian

 “LinkedIn Posting Made Easy with API" is a concise and informative video by @IOan. It explores the power of using an Application Programming Interface (API) to simplify and streamline the process of posting content on LinkedIn. With the rise of social media and the importance of professional networking, LinkedIn has become an invaluable platform for individuals and businesses alike. However, manually posting content on LinkedIn can be time-consuming and tedious. In this video, we delve into the benefits of leveraging an API to automate and optimize LinkedIn posting. From integrating your existing systems to seamlessly pushing content updates, the video demonstrates how APIs can enhance efficiency, save time, and expand your reach on LinkedIn. Whether you're a developer, a marketing professional, or an entrepreneur, "LinkedIn Posting Made Easy with API" provides valuable insights and practical tips for leveraging this powerful tool to maximize your LinkedIn presence.

I am experimenting with a new video format and wanted to share it with the community.Here is a video with a really simple yet smart Cypress tricks. 

Hey everyone,Guess what's happening in just two days? Yup, we're hosting a super cool webinar with @danielknott all about AI! 🤖💻 If you've got burning questions about AI and how it's shaking up the testing world, drop them in this chat. We'll make sure Daniel tackles them during the webinar.Get hyped and get those questions flowing! See you there!