How Enterprises Achieve QA Automation at Scale

Automated testing has solidified its position as the most widely adopted QA practice in 2024, with 87% of respondents identifying QA automation as a critical area of adoption, according to Perforce’s State of Continuous Testing.

And it makes sense that enterprises are focusing on automated QA testing. When done right, automation can help organizations ship high-quality software faster, reduce testing bottlenecks, and minimize production issues while overcoming technical complexity, infrastructure bottlenecks, and lack of resources. 

But getting your QA automation to scale? That is challenging. Legacy systems, tool sprawl, unreliable test data, and a lack of skilled resources are all obstacles that threaten to stand in the way.

In this guide, learn the best practices that enterprises need to achieve QA automation at scale. We’ll break down the biggest challenges organizations face when scaling test automation and the key components needed to overcome them.

Explore how BlazeMeter can help you scale your automated testing. Try it for free today>>

6 Challenges to Scalable QA Automation

While enterprises face a host of obstacles when delivering high-quality software, here are six of the key challenges developers and engineers face when scaling their QA:

1. Legacy Systems

Most enterprises are not starting from scratch with a clean, microservices-based architecture. Instead, most are working with legacy code and systems, monolithic applications, and brittle dependencies. Plus, these systems weren’t built with automation in mind, meaning tool integrations could be complex. 

Test automation that can scale needs to be able to work with your underlying architecture, not against it, requiring compatibility with legacy applications and creative thinking to overcome QA automation challenges.

2. The Wrong Test Automation Tools 

The test automation tool landscape is vast, with hundreds of tools covering different types of testing, environments, and systems. Each offers different capabilities and is built for different users and scenarios. The depth and breadth of choice make it difficult to choose the one that aligns with the organization’s tech stack and business needs. 

But choosing the right tool stack is essential for scaling. Ease of use, use case coverage, flexibility, and ability to consolidate multiple types of testing will determine how usable - and future-proof- the tool is for the organization’s needs.

3. Flaky and Unstable Test Scripts

Despite the best of intentions, automated tests often fail, especially as teams look to scale. Plus, automated test maintenance remains a major concern of enterprises looking to scale, with 36% of respondents in Perforce’s State of Continuous Testing highlighting automated script maintenance as a barrier to scalability. Whether it’s unstable locators, dynamic elements, or inconsistent environments, these and other factors can make test scripts unstable and produce unreliable results. In addition to wasted team effort, flaky scripts also deteriorate your overall code quality and slow down time-to-market. 

To meet the challenge of flaky and unstable test scripts, enterprises need to explore test automation tools that leverage AI to ensure test script quality and integrity. With groundbreaking capabilities such as AI validation and self-healing, AI is reshaping the ways teams scale QA automation without manually checking each test for issues.

4. Ensuring Consistent and Reliable Test Data

Automated tests require a consistent, reliable data set. But managing high quality and accurate test data across environments and keeping it up to date is a significant challenge due to costs, privacy issues, and overhead. QA automation tools should equip testers with solutions for creating synthetic data and for anonymizing data, so data doesn’t become a bottleneck for scalability. 

5. Lack of Skilled Resources and Expertise

Scaling automation requires skilled engineers, but many teams lack sufficient expertise in automation frameworks, scripting, and DevOps practices. Without the team who can run the tests and analyze results, testing is limited in its ability to scale to more code coverage and use cases. A testing vendor that makes testing easy for all stakeholders through a clear UI, assistive AI, and automated insights, along with resources for learning about testing, will help accelerate testing adoption and scale.

6. Integrating with CI/CD Pipelines

Tests must seamlessly integrate with CI/CD workflows to enable continuous testing, but many organizations struggle with broken pipelines and manual dependencies. Automation is key to scalability, since it runs tests and enables adding and removing tests quickly and without much effort. Therefore, testing tools should easily integrate with all CI/CD tools.

7 Key Components of Scalable QA Automation

To address the  challenges mentioned above, here are the 7 components that enterprises need to scale their QA automation effectively:

1. Testing Both Legacy and Modern Systems

Testing at scale requires solutions for testing various types of systems, whether microservices, monolith, serverless, etc., as well as support for components like mainframes, various databases, APIs, different protocols, etc. Testing should ensure all types of systems remain performant, scalable, and reliable even as modern applications evolve.

For example, JMeter can be used to test systems that rely on HTTP, FTP, JDBC (databases), SOAP, and even mainframe protocols. A solution that supports using JMeter at scale (like BlazeMeter) allows teams to make the most of the technological benefits of JMeter in an enterprise-fit business environment. This is the same for UI-based testing and integrations with Selenium and Cypress, along with other testing types.

Additional capabilities that support this are scriptless testing by recording user flows and automatically turning them into scripts, and service virtualization to simulate missing components, reduce dependencies, and enable testing without impacting production systems. Plus, enterprises looking to scale their QA automation require solutions that support testing in cloud, on-prem, and hybrid environments.

2. Support for Multiple Use Cases and Testing Types

Scalability drives agility and speed, but nothing slows down release cycles more than testers context switching between different testing systems for performance, API, UI, or functional testing. A unified solution streamlines testing by consolidating different testing methodologies into one platform, simplifying workflows, enhancing usability since developers are familiar with the platform and don’t waste time onboarding or learning how to operate it. This provides the organization with flexibility to add or remove testing types as needed.

3. Robust Test Validation

The quality of your tests will determine the quality of your application. Therefore, it’s important to ensure your testing tool allows robust test creation by offering capabilities like parameterization and correlation and AI-powered test optimization. You can also opt for scriptless testing, to reduce the risk of manual errors in scripting.

4. Test Data Management (TDM)

TDM features help teams efficiently generate, manage, and use test data across different testing scenarios. These capabilities ensure realistic, reusable, and secure test data for performance, functional, API, UI, and service virtualization testing.

AI further enhances TDM by automating synthetic data generation, enabling the creation of scalable, high-quality, and privacy-compliant test datasets without relying on seed lists. It also strengthens data security through AI-powered masking and anonymization, ensuring compliance with privacy regulations while preserving data utility. Additionally, AI optimizes testing by augmenting existing datasets with variations and intelligently selecting relevant data subsets, improving efficiency, accuracy, and scalability in software testing.

5. Scriptless Test Creation and Friendly UI

Testing solutions that are easy to use will democratize access to testing, increasing adoption across the organization and enhancing code quality. Example capabilities include scriptless test creation with recorders and converters, intuitive UI through step-by-step guides and drag-and-drop testing, automation, and integrations with familiar tools. In addition, AI-powered test analysis helps users automatically identify performance bottlenecks and areas of improvement.

It’s also helpful to find a tool with community support, through courses, blogs, online communities, a strong support team, etc.

6. CI/CD & Test Automation Integration

If testing isn’t embedded in CI/CD, it’s just another set of scripts that nobody runs. Choose a tool that can automated tests directly into Jenkins, GitHub Actions, GitLab CI, etc.and run as scheduled (e.g, after each build, every week, once a quarter, etc.).

7. AI & ML in Test Automation

AI is the future, and scaling QA requires the ability to embed AI in testing. This could include, for example, suggesting relevant test cases, auto-correcting tests, suggesting insights for fixing code based on test results, and more. While there is still a lot to be learned about AI’s capabilities, not integrating AI is the sure way to stay behind.

Here’s an overview of QA automation scaling challenges and solutions:

Bottom Line

When testing is not scalable, developers waste time debugging, customers receive faulty applications and the organization’s reputation as a quality provider is at risk. The right QA automation best practices and tools can help overcome these challenges, ensuring high quality software and accelerated delivery times for satisfied customers and developers.

Explore how BlazeMeter can help your organization scale your automated QA testing efforts. Try our platform for free today, or request a demo of our innovative AI capabilities. 

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