Why QA:Dev Ratios Don't Add Up

Omed Habib

July 13, 2023

Darkweb v2.0 public release is here

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What has changed in our latest release?

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All new features available for all public channel users

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Coding collaboration with over 200 users at once

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Real-time code save every 0.1 seconds

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Software engineering has evolved significantly over recent years, now characterized by complex distributed systems, cloud-native architectures, and an increased reliance on third-party services for development, testing, security, deployment, and maintenance. The rise of Agile methodologies and the widespread adoption of Continuous Integration and Continuous Delivery (CI/CD) pipelines have become standard, shortening development cycles and fostering the rapid delivery of high-quality software.

Despite these advancements, Quality Assurance (QA) remains the final hurdle that development teams must overcome before shipping their features. The importance of software quality has led many companies to rethink the traditional Developer to QA ratio. No longer a final checkpoint before shipping, QA is an integral part of the software development lifecycle.

The importance of software quality has led many companies to rethink the traditional Developer to QA ratio.

This changing mindset is evident in the rise of Shift Left testing and the increasing interest in leveraging AI to streamline the QA process. The modern testing landscape now requires experience using automated platforms. These tools help increase efficiency, reduce manual labor, and deliver faster results.

Shift Left or Get Left Behind

The "Shift Left" approach advocates for testing early and often in the development lifecycle. The earlier a bug is detected, the cheaper it is to fix. This approach, coupled with adopting AI and machine learning in testing, opens up new possibilities for improving efficiency and effectiveness in QA.

Modern testing tools can analyze vast amounts of data, identify patterns, predict where bugs might occur, and suggest preventative actions. Furthermore, the increasing difficulty in hiring traditional QA testers due to the evolving demands in their skillset pushes companies towards more automated and AI-driven testing methodologies.

Finding Balance

Determining the optimal Developer:QA ratio is a complex task. The right balance depends on various factors, including the project's size and complexity, the team's capabilities, and the desired speed of delivery. FAANG companies are rumored to be trending towards low QA to Dev ratios, sometimes as low as 1:10, indicating an industry-wide trend.

Yet, what works for one company may not work for another. Some organizations have seen success with Dev:QA ratios as low as 1:1, while others lean towards higher ratios. With the increasing demands on QA roles and the rise in testing automation, companies must continually reassess their approach to maintain an efficient and effective balance.

Automated Testing and AI

The trend seems clear: the incorporation of AI and automation in the QA process will continue to rise. Given the rapid pace of technological change and the increasing complexity of software systems, manual testing alone can no longer keep up. Automation is essential for conducting thorough and efficient testing that meets today's software demands.

Generative QA, specifically around unit tests, eliminates the QA to Developer ratio concept. Generative AI tools can quickly write thousands of unit tests that would have taken humans days or weeks to write. This not only increases test coverage but also reduces the amount of time developers spend on manual testing.

Generative QA, specifically around unit tests, eliminates the QA to Developer ratio concept.

By utilizing generative AI, companies can reduce the number of required QA engineers. Those QA developers can naturally shift their focus instead on writing features, not tests. At the same time, generative AI can help developers be more productive and efficient. By automatically generating tests, developers can focus on the tasks that require more creativity and higher-level thinking. In addition, the speed at which the tests are written and executed can significantly reduce the time it takes to detect bugs and other issues. This ultimately leads to less debugging and more time spent developing and improving the software.

Chasing the Mirage

The perfect Developer:QA ratio may be a myth. Instead of striving for an arbitrary ratio, companies should integrate QA into the development process seamlessly, leverage AI and automation where it makes sense, and build a culture that values quality at every stage of software development.

In a world where quality assurance is becoming increasingly critical, companies must invest in tools and methodologies that prioritize early and effective testing. By shifting left and leveraging the power of AI and automation, organizations can ensure that their software meets high-quality standards, irrespective of a (soon obsolete) Developer:QA ratio.