Discussions

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

I'm facing a unique challenge with automated testing of dynamic data in my test suite.We have a system where certain values, like user IDs, timestamps, or API response payloads, change with every execution. These values are unpredictable and can’t be hardcoded in our test scripts. I’ve tried using data-driven testing to handle these values, but the dynamic nature of the data is causing our tests to either fail or pass inconsistently.Here are the key challenges I’m dealing with:Handling unpredictable values (like timestamps) in assertions. Maintaining consistency across multiple test runs when the expected data is generated dynamically at runtime. Comparing API responses that contain dynamically generated values without false positives or negatives.Has anyone dealt with similar issues in their testing framework? What strategies or tools would you recommend to manage dynamic data and ensure stable test results?

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.

Test planning is crucial for ensuring comprehensive coverage, yet certain aspects are often overlooked. In your experience, what is the most commonly missed element in the test planning process? How does neglecting this aspect affect the overall test coverage and quality of the project? Share your insights on how to address this gap and improve the effectiveness of test plans.

Exploratory testing allows for flexibility and discovery, while structured test cases ensure thorough, repeatable coverage. Balancing the two can be challenging in a project. How do you manage both approaches in your testing process? Do you have any tips or strategies for finding an effective middle ground between exploratory freedom and the discipline of predefined test cases?

AI-based testing tools are gaining popularity due to their ability to improve test efficiency, accuracy, and coverage. Have you or your team integrated any AI-driven testing tools into your workflow? If so, how have these tools impacted the accuracy of your testing, reduced testing time, or helped in identifying potential defects earlier in the cycle? We're interested in learning about the practical benefits and challenges encountered with AI in testing.

Hi ShiftSync Community,I have one question related to the webinar for you:Name 3 AI features that software testers can already use today.One of you has the chance to win some nice swag provided by ShiftSync.  Best,Daniel

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?

Hello everyone,I’m excited to join this vibrant community!My name is Rahul Pandya, and I am a Senior Engineer based in Berlin, Germany. With 10 years of extensive experience in software testing, I am eager to explore and learn more about AI tools currently utilized in the market. I look forward to gaining insights from all the members and sharing knowledge to further enhance our practices in testing.Looking forward to connecting and learning together!

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

Ever been in a meeting with other developers, and they keep throwing around terms you’ve never heard of or are not sure off.What is your go to place to quickly get the best information on the subject:Google Wikipedia? Some other tools?See below 

Hi I have taken up AS1 Assessment 3-time buts not cleared  it is saying that need to wait for 2  to 3 months can you please help me that I am want to take up the test now to Enable to take up the test to whom i have to contact