Testing automationFramework migration

Framework migration pain: Why switching isn’t as easy as it seems

August 19, 2025
5 min read
By Ákos Jakub
#Selenium migration#Playwright migration

Switching testing frameworks often seems simple, but hidden costs and wasted effort make it a challenging process. Here's why smarter solutions like Bugninja matter.

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The illusion of 'just switching' frameworks

Switching testing frameworks often begins with the idea that it’s a straightforward process. Engineers, managers, or QA leads might think, “We’ll just replace Selenium with Playwright,” or “Let’s switch from Cypress to something more scalable.” The reality is far more complicated.

Framework migration is rarely as simple as swapping out one tool for another. Testing frameworks—whether Selenium, Cypress, or Playwright—are deeply integrated into your workflows, environments, and even team expertise. Migrating involves not only technical adjustments but a reassessment of how your team operates, how your tests are structured, and how your infrastructure supports them.

Take Playwright as an example. While it offers modern features like better cross-browser support and improved debugging tools, adapting your existing tests to Playwright’s structure is a time-intensive process. Legacy tests written in older frameworks often require rewriting—not just refactoring—because Playwright’s syntax and methods differ significantly. The illusion that migration is “just switching” often leads to underestimated timelines and frustrated teams.

Hidden costs of domain-specific knowledge

A major hurdle in framework migration is the domain-specific knowledge embedded into your existing processes. Over time, teams develop expertise in using a specific framework, building custom utilities, and solving problems peculiar to that tool. When you switch frameworks, much of this knowledge becomes obsolete.

For example, QA engineers proficient in Selenium may know how to troubleshoot its quirks, like handling dynamic elements or navigating flaky test behavior. A migration to Playwright means they must learn an entirely new set of quirks and best practices. This learning curve isn’t just about reading documentation—it’s about trial and error, debugging failures, and building confidence in the new framework.

Additionally, migrations often reveal gaps in knowledge that were masked by familiarity with the old framework. The team might realize they don’t fully understand cross-browser testing or asynchronous handling, which were simplified by the previous tool. These gaps add hours to the migration process, delaying progress and increasing costs.

The value gap during migration

Another often-overlooked challenge is the value gap—the period during migration where no tangible business impact is delivered. While your team is busy rewriting tests, learning new frameworks, and debugging issues, your product development stalls. This can be especially painful for startups and fast-moving companies where every sprint counts.

Consider a scenario where a startup decides to migrate from Cypress to Playwright to improve scalability. For weeks or months, the QA team is occupied with migration tasks, leaving little room for writing new tests or improving test coverage for ongoing features. During this time, bugs might slip through, impacting the user experience and potentially costing revenue.

This value gap is often underestimated in migration plans. Teams focus on technical details—like setting up the new framework and converting old tests—but fail to account for the opportunity cost of paused development. The longer the migration takes, the more this gap widens, leaving the company vulnerable to competitive pressures and customer dissatisfaction.

Why smarter top-to-bottom approaches reduce wasted effort

To avoid the pitfalls of framework migration, teams need smarter, top-to-bottom approaches that minimize wasted effort and maximize efficiency. This is where tools like Bugninja excel.

Bugninja provides an AI-powered testing platform that integrates seamlessly into existing workflows while offering advanced automation capabilities. Instead of forcing teams to rewrite tests manually, Bugninja helps automate the migration process by converting legacy tests into modern formats. Its intelligent algorithms identify gaps in test coverage, suggest optimizations, and streamline debugging.

Moreover, Bugninja reduces the learning curve by offering intuitive dashboards and actionable insights. Engineers don’t have to spend weeks mastering a new framework; Bugninja simplifies complex tasks, allowing teams to focus on delivering business value rather than wrestling with migration headaches.

Smarter approaches like Bugninja don’t just save time—they preserve momentum. Teams can continue testing new features, maintaining coverage, and releasing updates while the migration happens in the background. This balance between migration and development is crucial for startups and fast-paced organizations where agility defines success.

Forward-looking insight

Framework migration is undeniably complex, but the key to success lies in recognizing its challenges upfront and adopting smarter tools that address them. The illusion of “just switching” frameworks and the hidden costs of domain-specific knowledge are real—but they don’t have to be insurmountable.

As testing evolves, tools like Bugninja represent the future of automation and efficiency. By providing AI-driven solutions that simplify migration and enhance workflows, Bugninja enables teams to focus on what matters: delivering better products faster. The next generation of testing frameworks isn’t just about technology—it’s about smarter processes that empower teams to adapt, innovate, and thrive.

Try Bugninja for free and experience a smarter way to handle framework migration challenges: https://bugninja.ai.

About the Author

Ákos Jakub

Ákos Jakub

CTO @ Bugninja

Deep learning engineer and CTO with a passion for solving complex problems in unconventional ways. Worked as a quantitative developer at Morgan Stanley focusing on AI, and now leads engineering at Bugninja AI. Strong background in deep learning architectures, scalable ML systems, and applied NLP.

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