Scaling Test Automation in the Enterprise: The 5 Key Things to Consider

In today's fast-paced business environment, enterprises are under intense pressure to deliver applications faster while ensuring quality. 

How can organizations ship faster without compromising on test coverage and quality? How can we avoid jeopardizing the customer experience and ultimately our reputation, as we speed up and scale up?

In this article we explore the strategies for enterprise-scale testing to accelerate releases with quality. 

There are many variables to consider when defining your enterprise testing strategy, but from our experience working with hundreds of companies and QA professionals on a daily basis, we have distilled the 5 top considerations to scale your test automation. Read on! 

1. Application Scale

When you talk about the scale of an application, you must look beyond just the number of users that will access it. In fact, scale means much more, including:

• The technologies and programming languages used to build the application; 
• The infrastructure and servers that are used to host the application
• How the application is deployed, for example, is everything sitting in one place, or is it distributed?

All of these contribute to the application complexity and must be taken into consideration when defining how we’re going to test it. 

Whether using manual or automated testing, understanding what happens "under the hood" of an application is critical to know how to test it effectively at scale. 

With this knowledge, you can focus testing on components most susceptible to breakages in a given environment or configuration. This is a particular case where blanket test coverage is less efficient than targeted testing of specific elements.

2. Test Environments

Test environments (where you’re actually running your tests) are critical to automation success. What we find is that, more often than not, test environments differ drastically from production environments, which leads to inconsistent behavior and flaky test results. 

The rule here is simple: the more you can mirror real-world conditions in testing, the more confidence in your test results you will have. 

While Docker has helped standardize environments through containers, you still need to replicate factors like network architecture, access controls, and integration points. Leveraging infrastructure close to production, like within the same data center, gets you closest to the real user experience.

One important aspect to consider is how the applications are being accessed. External cloud testing services may require additional hops adding latency and compliance risks. Also, whitelisting vendor IPs or opening VPN tunnels creates security issues that you should avoid at all costs (or might not be allowed at all, depending on your company’s security policies).

Equally important is to perform effective cross-browser testing - having access to diverse browsers, devices, and OS versions that match what your customers actually use to access your applications. Maintaining this test lab requires ongoing effort as new versions of browsers are released, therefore make sure you take this into consideration and look for solutions that automatically support new browsers and versions. 

Without production-parity environments and real-world test coverage, bugs will inevitably reach users, therefore maintaining this test lab requires ongoing effort.

3. Test Data

Test data is make-or-break for automation. Your application behavior directly corresponds to the data it receives. When you look at automation, you want your tests to be repeatable, and reliable - therefore, the same data inputs should yield identical outputs every time. 

Synthetic test data generation works for many cases, and when possible should be the preferred way to go. Unfortunately, as applications grow in complexity and in large enterprises, synthetic test data can often fall short. In those cases, real or realistic data is needed to exercise complex scenarios. Taking periodic snapshots of production data helps, but presents challenges:

• Test executions modify the data state causing inconsistent results on subsequent runs
• Production data drifts over time as users interact with the live system
• Difficult to mask sensitive information, needed for compliance

The ideal solution provides access to real-world data states without impacting production or previous test runs. 

If you need to use real data in test scripts, be aware of what is allowed from a privacy standpoint. Especially if test data leaves your network, you must understand exactly what data it is, where it's stored, for how long, and who can access it… 

You need to ensure that using real data doesn't breach any data privacy regulations. If all your testing infrastructure and tools are within your corporate network, there may be less risk. However, you still need to be thoughtful about using real or realistic data in tests (always check with your DPO or equivalent before even considering it!). 

4. Test Automation Tooling

Automation can be a fun and engaging project, but it's important to stay focused on the end goal - delighting your customers. Avoid automating for automation's sake or tackling overly complex test cases just because they are challenging. Instead, focus automation efforts on your core business flows and priority user journeys. This ensures you are automating what matters most.

With the proliferation of testing tools, it's tempting to adopt the latest and greatest without much diligence. However, take time to thoroughly research tools rather than jump on trends. Evaluate if a tool truly meets your needs and integrates well with your tech stack. For open source options, look at contributor activity, sponsorship, and other signs of an active, stable project. 

For enterprise teams, you want stability and longevity in your tooling choices. Do due diligence to pick solutions that will continue serving your needs for years to come.

When selecting test automation tools, parallel execution capabilities should be a top priority. Running tests in parallel drastically reduces overall test execution time compared to sequential execution. To enable fast feedback loops and frequent testing, results need to be delivered quickly. Waiting days or even overnight for test suites to run is not feasible. 

Support for parallel testing is critical since you need to test across browsers, devices, and platforms, which explodes the number of tests you need to execute. Many tools are designed for sequential use only and especially for an enterprise you will hit limitations as test volume increases. 

Parallel testing also places more emphasis on test data strategy. With tests running concurrently across configurations, test data needs to be properly provisioned and managed for each parallel execution. 

Bottom line: ensure any tool you choose can scale to meet your parallel testing needs now and in the future! 

When evaluating tools, look at adoption trends and seek input from other users further along in their automation journey. Larger enterprises may stretch new tools and uncover limitations or pitfalls. While it can be tempting to jump on the latest trends, it is risky to be the earliest adopter of a new automation framework. Leverage the experience of others to make informed decisions.

5. Continuous Integration

Continuous integration is critical for bringing automation strategies together and enabling rapid feedback. The goal is to commit code and get feedback on whether the application behavior has changed, in just minutes (which is key for efficient Regression Testing). 

Without continuous integration, enterprise projects can accumulate technical debt for weeks before problems surface, making issues much harder to pinpoint and fix. Continuous integration forces you to validate changes early and often.

To get the full benefits, testing infrastructure must be performant and reliable. Fragile or slow environments hamper the speed you need. Leveraging a well-maintained grid within your corporate network ensures high performance and consistent test execution. 

Conclusion:

By focusing on these 5 key aspects of test automation, QA teams can maximize the return of their test strategy, ensuring that application release cycles accelerate while maximizing the quality of the applications being delivered. The result will ultimately be customer delight which, at the end of the day, is why we do all of this in the first place!

‍

FAQs

1. How do organizations prioritize which tests to automate first when scaling test automation efforts?

‍Prioritization often involves identifying tests that provide the highest value in terms of coverage and frequency of use, such as core functionality tests, and those that are most prone to human error if done manually.

2. What are some common pitfalls to avoid when scaling test automation across multiple teams or departments?‍

Avoiding silos of knowledge and ensuring consistent practices across teams are crucial. It's also important to manage dependencies effectively to prevent bottlenecks in the automation process. It’s also important to leveraging the same technologies and tool sets across teams, not only for easier knowledge sharing but also from a cost effectiveness perspective.

3. How can enterprises ensure their test automation strategy remains aligned with their overall business objectives as both evolve?‍

Regular reviews and adjustments of the test automation strategy in light of changing business goals, technologies, and market conditions help maintain alignment. Engaging stakeholders across the business can provide valuable insights into strategic alignment.

4. What are some examples of metrics or KPIs that are useful for measuring the success of scaling test automation efforts?

Useful metrics include the reduction in regression testing time, increased test coverage, the frequency of defects found before production, and the time taken to release new features.