Software testing isn’t always glamorous, but it is essential. A world without software testing is scary, and technology would be nowhere near as advanced as today. However, as important as this process is, it’s not easy.
End-to-end testing is an important step in software development, where the whole system is tested from start to finish to ensure that it works as expected. While extremely important, the labor of end-to-end testing leaves it needing data-driven approaches to optimize it for the better.
Combining end-to-end testing and AI offers software development teams ways to automate and improve the E2E testing process by providing a more comprehensive and efficient approach. AI can detect and diagnose problems faster, reduce manual effort, and provide more accurate results.
End-to-End Testing’s Strengths and Shortcomings without AI
Naturally, nothing in life is perfect. Even with end-to-end testing, some unique pros and cons come along. However, these shouldn’t be studied in a vacuum — every software development team is unique, and how they work is unique too.
End-to-end tests are a comprehensive approach to ensuring your software runs as expected before hitting production. Standard end-to-end testing helps teams:
Ensures the Quality Of Your Application: With end-to-end testing, your teams can ensure that your entire application, from end to end (see what we did there?), is up to snuff. This is a great way to see how your software performs in a production-like environment, allowing you to test safely without risking releasing a problematic build to the public.
Eliminates Integration Issues: Since end-to-end testing is the first time your whole system is integrated with all of its individual parts, it allows you to see how they perform. It also allows you to test whether or not everything works as intended — as you may end up having a data stream end abruptly or a missed connection somewhere that wouldn’t be spotted without end-to-end testing.
Prevents Performance Issues: With this step being the first time your software is fully assembled, it offers an excellent opportunity to test and spot any performance issues lurking in your code. Without it, you can quickly end up with a significant problem in production or a slow, unsatisfying product.
Provides Complete Coverage: End-to-end testing offers your team a smooth segue into total test coverage. Since it’s the last step before production, and there are now so many connections between different systems and integrations, it can quickly get out of hand by just relying on existing unit tests. With end-to-end testing, you get a complete picture of your application and can target the areas your other tests couldn’t.
While end-to-end tests ensure coverage and reduce integration and performance issues, they are costly in time, expense, and resources. Standard approaches to end-to-end testing suffer from:
Heavy Time-Consumption: Unfortunately, when it comes to end-to-end testing, it’s a lengthy process. Tests are nowhere near as fast in this step but cover much more ground.
Bloated Costs: Time is money. It’s not surprising that there are budgetary pressures with end-to-end testing. These tests take time to create, which costs person-hours. It can also require specialized tools, which can be expensive to purchase and integrate into your existing workflow.
Complexity & Resourcing: End-to-end testing is complex as it involves testing the entire system and all its components, which can be difficult to manage and coordinate. The more complex your testing, the more resources are needed to ensure errors do not get overlooked.
Difficult Error Detection: It can be difficult to debug end-to-end tests as it involves testing the entire system and all its components. This makes it difficult to identify the source of any errors or failures. With how extensive end-to-end testing is, it can quickly become an arduous task. This ties into our last three points — they all lead to a complicated process but ultimately result in a far better final product.
These issues harm release quality and speed, especially when unnecessary and flaky tests aren’t dealt with earlier in SDLC.
The Real Potential of End-to-End Testing and AI
You can’t just plug your code into ChatGPT and hope it’ll streamline your testing workflows and identify errors. Adopting AI and automation into your software testing workflow can make massive improvements.
Combining end-to-end testing and AI can make your most complex testing data-driven for faster testing cycles, increased accuracy and reliability, and improved scalability.
Launchable helps teams incorporate AI intelligence into their test selection and monitoring workflows for faster releases. Launchable’s Predictive Test Selection empowers teams to:
|Get Feedback Faster to help your QA team focus on what’s working and what isn’t.||Improve Developer Experience by ensuring your releases go smoothly to production using data-driven test selection.||Lower Infrastructure Costs by giving insights into your testing data and running smaller tests during the testing lifecycle.|
The power of end-to-end testing and AI also includes the ability for teams to gain powerful Test Suite Insights. With better visibility into the health of test suites, developers can:
|Defeat Flaky Tests by highlighting them for your team to fix before they become unmanageable.||Track Important KPIs to paint a full picture of how your testing lifecycle is thriving and what needs improvement.||Spot Failures with clear, organized visibility into your test suite and individual test outcomes.|
With data-driven test selection and insights, Launchable helps teams make their testing data-driven to speed up releases and lessen budget, time, and resource expenses. Blending Launchable into your testing dramatically increases testing reliability and speeds up your testing process as a whole, giving you the push you need to cross that finish line every time.
TLDR on End-to-End Testing and AI
End-to-end testing is a complex, often lengthy process, but it’s incredibly powerful, ensuring your releases don’t let defects escape. Teams prioritizing making their test suite data-driven include end-to-end testing and AI to speed up their test cycles without sacrificing quality.