5 Common End to End Testing Challenges and How to Fix Them

Imagine you're plotting each different type of software test on a 2x2 matrix - an 'opposing characteristics framework' to give it its full title.

Along one axis, you measure value. Along the other, you measure resource intensity. Where do you think, traditionally, end to end testing would sit?

A few years back at least, we'd have placed it in the 'high resource, high value' box too. That's to say, whilst you get some extremely useful insights from end to end testing, it can be expensive, time consuming and not without its challenges.

Good news: it doesn't have to be. By working a little smarter and taking advantage of recent leaps in AI testing tech, you can shift end to end testing from that valuable-but-frustrating 'high resource, high value' to the much coveted 'low resource, high value' quadrant.

Here's how to address 5 common end to end testing challenges to the same valuable insights with a fraction of the cost and time.

1. Complex test environment setup

The issue: the point of end to end testing is to check that your app and its dependencies work as they should do, from start to finish. Naturally, this means your test environment will be made up of numerous components, including front-end interfaces, back-end servers, APIs, databases, and cloud services.

These complex test environments - and the scripts that run on them - take significant resources to build, maintain, and run, regardless of whether you outsource (expensive) or use your in-house resources (time pressure).

How to resolve: smart use of containerization solutions like Docker can help you standardize test environments. They also ensure that complex end to end test scripts don't use excessive system resources.

You can take things to the next level by using an AI testing tool to automate end to end tests and build test scripts with minimal coding. Tools like Momentic allow you to test in any environment, use plain English to build test scripts, and test flows involving SMS or email without the need for complex scripts. This makes managing complex test environments significantly easier and reduces the resources needed.

2. Outsourcing and integration with CI/CD pipelines

The issue: increasingly, CI/CD is everything to businesses looking for a strong competitive difference in crowded digital markets. Your ability to launch new products, release updates, fix bugs, and react to external circumstances all hinge on strong CI/CD pipelines that allow you to ship code multiple times per day.

Due to the complexity of the tests and the resources needed to maintain them, many businesses choose to outsource their end to end testing to external QA teams, or maintain a separate software testing team internally. Both of these options slow down release cycles and create issues with CI/CD pipeline integration.

The solution: is 2025 the year you say goodbye to external QA? AI testing tools allow you to complete the Software Testing Life Cycle in a fraction of the time it otherwise takes. This drastically reduces the need for additional resources - your developers can set up and run tests themselves in a fraction of the time, and implement fixes immediately.

What's more, these AI testing tools can integrate directly with your CI/CD pipeline for end to end automation. Momentic integrates with widely used tools like GitHub Actions, CircleCI, and GitLab Pipelines. No need for any complex workarounds to accommodate external teams, or delays as you wait for results - just simple, continuous, and immediate integration whenever needed.

3. Long execution times

The issue: end to end tests verify…well…end to end functions. You're not just testing a few units of code - you're figuring out whether your app works in its entirety and can perform all the functionality laid out in your requirements document.

This can translate into lengthy run times for end to end tests and make regular testing - a vital component of CI/CD - increasingly difficult to perform as your application grows. If left unchecked, end to end tests create a bottleneck towards the end of development, causing frustration from business-focused and tech-focused teams alike.

The solution: completely automating your end to end tests goes a long way in reducing how long they take to complete - particularly if you use a low-code solution to minimize hours spent on script creation and maintenance. But there's plenty more you can do here.

By running tests in parallel, you can cut the total time needed for end to end tests by at least 50%. And you can save even more time by being smart about what you test - prioritize testing critical functions and, when launching updates, focus on tests most relevant to recent code changes.

4. Flaky tests and unreliable results

The issue: end to end tests are notoriously flaky. Even the most skillfully crafted test scripts can flake for a number of reasons, including failure of external dependencies, unstable environments, limited hardware or network bandwidth, and timing events around asynchronous activities.

This has the potential to wreak all sorts of havoc with your testing results, from false positives to missed bugs. Flaky tests can undermine your organization's trust in the testing process, waste valuable resources, and negate any positives you'd expect to see from end to end testing.

The solution: there are numerous ways to minimize test unreliability. Using a containerization tool to ensure your test environment is as stable as possible, adding 'retry' options for key types of test failure, and improving test design to accommodate timing issues can all increase the reliability of end to end test scripts and the results they produce.

AI testing tools also offer automated test maintenance. As well as fixing flaky tests automatically, these use machine learning to adapt to your specific applications. Your engineers get valuable time back to spend on more valuable work, whilst your tests run increasingly smoother the more you automate.

5. Test data management

The issue: good data is fundamental to running relevant and useful end to end tests, but sourcing, maintaining, and securely storing this data can be tricky. Firstly, you'll need to ensure that data is kept consistent across different environments - and for distributed systems and microservices architectures, you'll need to make sure data synchronises across multiple services and databases.

After that, you'll need to address how to manage and store the vast datasets that end to end tests may need, create datasets that account for complex interdependencies between systems, maintain data integrity, and ensure data quality as your application (and by extension your test suite) scales. Sounds exhausting, right?

The solution: implementing concrete processes for creating, managing, and maintaining test data sets is everything here. Use real world data where possible - but use masking or anonymization tools to maintain privacy. Implement version control for complex datasets to ensure consistency, and automate data validation checks to reduce human error.

If your tests need more data than you currently hold, or are willing to commit to storing safely, there are a number of automated tools you can use to generate realistic test data based on predefined rules. This is great for expanding your test coverage to a wider range of cases with minimal extra time spend.

Momentic: Your Engineers' E2E Testing Cheat Code?

"Momentic makes it 3x faster for our team to write and maintain end to end tests."
Alex Cui, CTO, GPTZero

We'd love to see if Momentic's AI testing tools could help you optimize your software testing life cycle.

If, like Alex and his team, you're keen to save over two thirds of the time you spend on key testing processes, why not schedule a conversation with our founder?