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See Why Your Testing Framework Is Incorrect, Incomplete, or Inefficient — And I’ll Show You Why
See Why Your Testing Framework Is Incorrect, Incomplete, or Inefficient — And I’ll Show You Why
Playlist
- See Why Your Testing Framework Is Incorrect, Incomplete, or Inefficient — And I’ll Show You Why
- Transitioning from Manual Testing to Test Automation with Cypress
- Shift Happens: Driving Quality Left—A Real-World Journey Across Five Teams
- Client Feedback & Quality Assurance in Web Design for Agencies
- Democratise Automation to Build Autonomy and Go-To-Market Faster
- Testing AWS applications locally and on CI with LocalStack
- Man Vs Machine: Finding (replicable) bugs post-release
- Shift-Left: Accelerating Quality Assurance in Agile Environments
- End-to-End Test Automation with Provar
- Fast-Tracking Project Delivery:Tips from a Recovering Perfectionist
- Crawl, Walk, Run...Fly - Take your build and test pipeline to the next level
- Revolutionizing Testing with Test Automation as a Service (TaaS)
- How Codemagic Mitigates Challenging Mobile App Testing Environments
- Fast and Furious: The Psychology of Web Performance
- Faster Feedback with Intelligent CD Pipelines
- Navigating the Future of Quality Engineering in 2024
- Using AI for Effective Test Generation
- Automated Test Execution Reporting
- How To Speed Up Our Work During Web Automation
- Building Products that Drive Better Results with Shortcut
- Shifting Accessibility Testing Left with LambdaTest and Evinced
- Testing Tomorrow: Unravelling the AI in QA Beyond Automation
- Flaky Tests from an Engineering Perspective
- Future Trends and Innovations in Gen AI for Quality Engineering
- Mastering User-Centric Mindset Unlocking Your Potential as a Tester
- From Brainwave to Inbox: Avo's Whimsical Adventure through AI-Powered Test Automation
- The Myth of ‘Best Practice’
- Impact and Potentials of GenAI to the IT Engineers
- Innovation Accelerated: The Intersection of AI and Quality Engineering
- Optimize Issue Tracking: Integrating SpiraTeam with LambdaTest
- The Power of Generative AI in Reducing Maintenance and Enhancing Speed
- Rethinking the Role of QA Profile
- Building Quality Software: AI-based testing approach with Jira and QMetry
- In-Depth with Playwright: A Modern Testing Framework
- Supercharge Your Data Quality Testing with AI/ML
- GenAI for Quality Transformation
- Creating Reliable and Scalable Test Automation Frameworks
- Upskilling Quality Engineers: A Success Story in SDET Transformation
- AI-Readiness: Are You Building the Future or Falling Behind
- Leading the Charge in Software Quality with Zero Bug Revolution
- Building a Test Automation Framework for TV Apps & Scaling at FX Digital
- Building High-Quality Teams: People, Process & Proof for QA Leadership
- Why Do We Have Bugs, and Why Do They Happen?
- GenAI in QA: Tiket's Approach to Evolving Quality Engineering
- Collaborative Remote Testing: How to Set Up & Run Effective Ensemble Sessions
- Simulating Real-World Scenarios: Balancing Precision and Practicality in Testing
- Building AI-Driven Test Automation Frameworks for QA Excellence : Challenges with Agile, DevOps, and AI-driven Testing
- Reinforcing Cybersecurity Beyond Functional Testing
- Building AI-Driven Test Automation Frameworks for QA Excellence
About The Video
Watch this episode of XP Series featuring 𝐊𝐚𝐫𝐥𝐚 𝐌𝐢𝐞𝐬𝐞𝐬, Head of Quality Engineering, Orbem.
🎙️In this episode, watch how Karla uncovers the hidden pitfalls of testing frameworks and how they impact software quality.
Also, learn how to identify red flags like recurring test failures, excessive maintenance, and poor test coverage that can slow your team down. This session dives into practical strategies to assess, refine, and optimize your testing framework for improved productivity and ensure higher-quality software releases.
Video Chapters
00:00 Welcome
00:52 Guest Introduction
02:09 Identifying Warning Signs in Testing Frameworks
05:36 Red Flags of Incomplete Testing Frameworks
08:46 Choosing the Right Testing Framework
11:24 Impact of Ineffective Frameworks on Development Teams
13:40 Consequences of Inefficiencies on Software Quality
16:35 Key Steps to Ensure While Refactoring Testing Frameworks
21:04 Common Pitfalls in Testing Framework Design
28:23 Real-World Examples of Resolving Framework Challenges
29:37 Conclusion
Key Topics Covered
Identifying Warning Signs of Inefficient Test Frameworks
Common issues include flaky tests, slow execution times, excessive test maintenance, and overlapping manual and automated tests.
Teams should ask critical questions to assess inefficiencies, such as whether automation engineers and manual testers are duplicating efforts.
Understanding When a Testing Framework is Incomplete
If tests require too many manual steps before execution, the framework may not be scalable.
Lack of clear tagging, categorization, or missing test layers (unit, integration, end-to-end) indicates gaps in test coverage.
Ensuring strong communication between manual testers and automation engineers is key to avoiding redundant work.
Recognizing When You Are Using the Wrong Testing Framework
The framework should be adaptable to different platforms (e.g., web, mobile, desktop).
Mismatch between development tech stack and testing framework can hinder collaboration and efficiency.
Documentation and community support are essential in ensuring long-term success.
How a Poor Testing Framework Affects Developers and Testers
Leads to frustration, burnout, and loss of trust in automation efforts.
Flaky tests make engineers ignore failures, which can hide critical bugs.
An inefficient framework increases manual testing workload, negating the benefits of automation.
Refactoring Your Testing Framework for Better Efficiency
If tests never detect bugs, they might be ineffective and need improvement.
Define clear goals before refactoring (e.g., speed optimization, reducing redundancy, or framework migration).
Gradual migration is advised rather than overhauling everything at once.
Common Mistakes When Designing a Testing Framework
Hiring automation engineers too early or too late can impact implementation success.
Lack of collaboration with developers leads to poorly designed test data structures.
Not thinking holistically about the testing ecosystem leads to redundancy and inefficiency.
The Impact of an Inefficient Testing Framework on Software Quality
If automation tests don’t provide value, manual testers end up carrying more workload, increasing risk and delays.
Poor framework design leads to high maintenance costs and slows down software releases.
A structured and well-optimized framework helps identify bugs earlier, improving overall software reliability.
Related Blogs & Hubs
LambdaTest
LambdaTest is an AI-Native test orchestration and execution platform that allows you to perform both manual and automated testing across 3000+ environments, making it a top choice among other cloud testing platforms.
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