The success of software testing relies on two things – the ability to detect bugs quickly, and the efficiency with which we can identify their root causes. Neither works without the other, but many teams focus heavily on detection, not realizing it’s only half the journey.
This is especially true in the world of automation. High test automation can uncover issues at scale, but without observability, those failures often become black boxes – difficult to interpret or act on. To truly reap the benefits of automation, we need both: broad detection through automated tests and deep visibility through observability.
The hidden cost of the overgrown automation
There is a common misconception that high automation coverage alone guarantees successful software testing. It’s a trap many teams (and especially managers under delivery pressure) fall into, aiming to quickly achieve impressive results.
90% regression tests coverage sounds great, until you realise they generate more noise and data overload than your team can handle. Thus, it becomes a paradox – the very thing that was supposed to help you save time, ends up making you lose it instead, spending countless hours trying to make something out of the flood of data.
The misconception:
High automation coverage = successful testing
Poorly observable automation wastes time by generating flakiness and undermining your trust in test results. Imagine two teams, both boasting 85% automation coverage. On paper, they look equally successful.
But when failures happen, their realities couldn’t be more different.
- Team A spends hours digging through cryptic logs, rerunning tests, and debating whether the problem is in the code, the environment, or the test itself. Their automation coverage is high, but their productivity plummets as they firefight flaky failures.
- Team B, on the other hand, has built observability into their automation. Each test logs detailed context, links back to the related commit, and provides clear artifacts like screenshots or traces. When a failure occurs, they can pinpoint the root cause within minutes.
The difference lies not in the level of automation, but the level of observability. Coverage alone creates the illusion of safety. Coverage plus observability creates confidence and speed. And just like with shift-left testing, the human factor matters – real collaboration between developers and testers is what makes these practices truly effective. Here’s why shift-left testing fails without that collaboration →
The truth:
Coverage + observability = successful testing
The challenge with higher-level tests
Another thing to consider is that not all tests are created equal. Unit tests are small and focused, checking one function or component in isolation. When they fail, the cause is usually clear and easy to pinpoint.
But as you move up the testing pyramid, things get more complicated. Integration and end-to-end tests cover larger workflows and rely on many dependencies. With so many moving parts, a single hiccup in any layer can cause the test to fail.
The higher the complexity of your tests, the more essential observability becomes. It provides the context needed to quickly identify the source of a problem, even in high-level testing.
What observability in testing actually looks like
Talking about “observability” in testing can feel abstract, but actually you need to follow just one principle:
Testing is not purely informational.
It needs to produce actionable insights in order to be effective.
In other words, just knowing that there are bugs is not enough – you must also know as much as possible about those bugs in order to quickly fix them. It is worth always keeping this thought at the back of your mind, and having it guide you when making decisions.
Monitoring vs observability
Some of the concepts and metrics related to observability can be recognizable for those familiar with monitoring – another essential practice in software testing. The two tactics might seem similar – that’s because they are just parts of the same effort – ensuring that your software is healthy and bug-free.
Their methods and scope of surveillance, however, differ. While monitoring tells you that something went wrong, observability helps you understand why. In the context of automated testing, this means going beyond dashboards that simply report test pass/fail rates and instead providing the rich context needed to explain those outcomes.
This difference directly impacts the possible outcomes – with monitoring you can react to the failures once they happen, following a pretty narrow path of predictable issues. Observability, on the other hand, equips you with a broad spectrum of data, enabling you to quickly react even in unforeseen circumstances.
Monitoring = reactive stance
Observability = proactive stance
Three pillars of observability
Okay, but how to actually apply observability in your testing? If simply running a bunch of automatic tests is only a part of the solution, what are the other steps that will get you closer to the informed testing mastery?
Three pillars of observability are the fundamental indicators that you should be working with to improve your testing observability. Each of them answers a slightly different question:
- Metrics: What’s happening?
- Logs: Where is it happening?
- Traces: Why is it happening?
Together, they provide you with a full spectrum of the problem, fetching crucial data on the state of your software, while at the same time gathering necessary contextual information.
| Pillar | What it is | How it helps in testing |
|---|---|---|
| Metrics | Numeric measurements over time (counts, rates, durations). | Show trends like failure rates, test execution times, or flakiness patterns across runs. |
| Logs | Detailed, timestamped records of events. | Provide context around test failures (e.g., what data was used, which dependency failed). |
| Traces | End-to-end record of a request’s journey through the system. | Pinpoint exactly where in a workflow a test failed – e.g., login succeeded, but the checkout service timed out. |
How to apply observability in automatic testing?
Effective observability is all about asking the right questions and making sure we have the means to find answers. In automated testing it’s especially important to plan these things out in advance and correctly set up your testing tool. Otherwise, you simply won’t get the information you need.
Question 1: What’s the problem?
How to find the answer: Enrich test logs with context
Don’t use default cryptic test logs. Make sure they include information about test data, environment, dependencies, and configuration. Remember that your aim is to keep a comprehensive record of testing results, not just a list of failed or passed tests.
Example:
“Login test failed.” ❌
“Login test failed with user X on environment Y because API Z returned 504.” ✔️
Question 2: What does the problem look like?
How to find the answer: Capture artifacts automatically
Most of us are visual creatures, and so sometimes descriptions alone are not enough to explain the issue, especially when it’s highly complex. In such cases, it proves effective to capture artifacts, such as screenshots, videos, API requests/responses, etc. to provide immediate visual or data-based clues, and reduce the need of rerunning the test.
Question 3: Who or what introduced the problem?
How to find the answer: Link failures to code changes
If multiple people make frequent changes to the code and suddenly something goes wrong, identifying the origin of the issue and the person responsible for the faulty change can turn into a wild goose chase. For that reason, linking failures to code changes can significantly reduce the time from detecting the bug to identifying where the problem stems from.
Question 4: Where in the system did the problem happen?
How to find the answer: Add tracing to test execution
Sometimes the whole chain of events fails, and it’s crucial to quickly identify which part of the process is responsible. To detect it, you can use distributed tracing that follows a request through multiple services during a test. If an end-to-end test fails, tracing shows where it failed in the chain.
If you’re using Jira for your testing purposes, you can link failures to specific parts of the system, making them easier to trace.
Question 5: Can I trust this test result?
How to find the answer: Track and analyze flaky tests
Automated tests don’t always fail for the same reason. Sometimes a test fails randomly, even though the software is actually fine. Observability lets you see patterns in these flaky failures instead of treating each one as a mysterious problem.
Question 6: Is the failure caused by my code, or by the system’s current state?
How to find the answer: Integrate with monitoring systems
When test results are fed into dashboards alongside production metrics, you can quickly spot correlations. For example, if CI tests fail at the same time memory usage spikes, you’ll know the issue is not in your code, but in the system’s health.
Question 7: How useful is my testing process, and how fast can we act on failures?
How to find the answer: Define actionable metrics
All the above actions are designed to support your team in the critical moments, when something goes wrong and needs to be fixed as soon as possible. But for your crisis response to be effective, you need to also:
- Track how long it takes to understand a failure (Mean Time to Understand, or MTTU).
- Watch for recurring failure patterns across different environments.
- Measure the impact of failures on release readiness.
These insights show not only how effective your testing is, but also how quickly your team can respond when an issue occurs.
Conclusion
High automation coverage without observability risks creating more confusion than clarity. Automation detects problems; observability explains them. One without the other is incomplete.
The lesson is simple: successful testing isn’t just about running a lot of automated tests. It’s about making those tests transparent, reliable, and actionable. In practice, that means pairing automation with observability – so failures become opportunities for improvement, not bottlenecks of frustration.
Start with asking some of the questions listed in this article, and see what happens. You’ll quickly notice that instead of getting stuck on vague red test results, your team gains the insights needed to act with confidence.
Effective testing isn’t just about running more tests – it’s about understanding them. Learn how QAlity Plus can help →
