How to Increase Test Coverage: Everything You Need to Know

Your organization understands how crucial automated testing is for delivering high quality software quickly. Good. Your organization also has a testing strategy in place. Even better. At this stage, a natural next step would be to think of ways to increase test coverage.

Increasing test coverage is crucial because you want your tests to help you catch defects earlier, avoid regressions, and ensure great quality. To achieve all of that, your tests must be comprehensive and cover as much ground as possible.

In this guide, you’ll learn:

• What test coverage is
• Why it’s important
• Best practices to increase it
• And how AI helps

Let’s dig in.

What Does Test Coverage Mean?

Test coverage is a way to assess how well your tests exercise the system under test from the point of view of the user's requirements and the expected behavior of the application. 

In other words, your application has good test coverage if you can say with confidence that all of the main use cases, flows, and scenarios have been mapped to tests.

Why does test coverage matter? It’s all about confidence. You probably want to know which parts of your application have been thoroughly tested and which ones are undertested. It helps you decide which features to release and when, as well as when to prioritize future testing.

If you’re interested in learning more about code coverage, you can read our previous guide.

How to Increase Test Coverage

By now, it should be clear that test coverage is a must if you want to keep producing high-quality software sustainably. How can you increase it, though?

Here is a non-exhaustive list of best practices you can follow.

1. If a Bug Is Found, Create a Failing Test Before Fixing It

When a bug makes it to production, it doesn’t feel great. But you can and should prevent the same bug from ever showing up again. You can do this by creating an automated test that exposes the bug. After you fix the bug, the test will pass and should remain like that.

The earlier you can get started writing test cases, the better.

2. Shift the Writing of Test Cases Left

The earlier you can get started writing test cases, the better. If your organization has a dedicated QA department, get them involved earlier in the process, maybe even pairing them with developers during the refining of stories. 

This will certainly produce more—and better—ideas for tests.

3. Review Test Cases Written for Past Similar Features

If feasible, go through test cases created for similar features in the past. With the volume of information we have to deal with daily, it’s often easy to forget the work we’ve done before. Getting inspired by your previous work when writing test cases will certainly be of help.

4. Map Acceptance Criteria to Test Cases

You’re creating software in 2026, which means that you’re likely using some flavor of agile as your methodology of choice. That means that you’re probably familiar with and use some concept of acceptance criteria (AC).

The suggestion here is simple: map your AC to tests. Ideally, each criterion should give origin to at least one test case. 

Remember to also test the “opposite” of an AC when it’s applicable. Let me explain: if one of the ACs for your file upload feature is that only .pdf and .jpg files are allowed, you should also write test cases for disallowed file types. 

This ties nicely into the next suggestions.

Testing only the happy path is a common mistake you must avoid if you’re to increase your code coverage. 

5. Test More Than the Happy Path

Testing only the happy path is a common mistake you must avoid if you’re to increase your code coverage. Remember that, in real life, things can and do go wrong. That means you’ll want to verify how your system reacts to things like

• Bad inputs
• Networking failures
• Failures from third-party dependencies
• Data corruption
• Unexpected increase in requests
• Larger data volume than expected

Chaos engineering is a valuable technique here.

It’s just as important to test that your error handling works as expected. If the system should send an email alert and write a log entry when a given error happens, ensure you can force one instance of such an error and verify that the email and the log entry were produced.

6. Use a Risk-Based Approach

Many people don’t have as many resources as they’d like when investing in testing. That means you’ll need to prioritize somehow.

A great suggestion here is to use a risk-based approach. Evaluate areas of your application based on

• How critical that area is
• The current coverage of the area
• How often it gets changed

Areas of your application that contain critical workflows and are changed quite frequently deserve more attention than areas that aren’t as critical and/or don’t receive as many changes. 

By applying a risk-based approach, you ensure your coverage is always increasing while making efficient usage of the resources you have.

The Role of AI in Increasing Test Coverage

So far, I’ve limited myself to “traditional” tactics when discussing best practices to increase coverage. AI wasn’t mentioned at all.

That was by design. AI is certainly the topic of the moment in tech, and I wanted to give it a star treatment by dedicating a whole section to it. You’ll now see how AI can be your best friend when it comes to increasing test coverage. 

1. AI Makes Tests More Robust

There are many common “pains” when it comes to implementing and maintaining a testing strategy. AI-powered testing solves many of those.

For instance, testing maintenance can be a heavy burden. That's particularly true when it comes to end-to-end testing, which can be quite brittle. 

All it takes is for someone to change an ID or CSS class, and your tests break. AI testing tools with self-healing and automatic fixing capabilities such as Autify Nexus make your tests more robust.

AI testing tools with self-healing and automatic fixing capabilities make your tests more robust.

2. AI Can Handle Complex Workflows That Traditional Tools Cannot

Tests that exercise the application using the UI used to be hard to automate. There are often complex workflows that real people use—such as canvas drawing, upload/download of files, working across different platforms/devices—that traditional automation struggles with. 

Advanced AI testing agents, such as Aximo, can help in those scenarios. By acting like a completely autonomous agent that “sees” the screen like a human would, this agent can test applications with no scripting or maintenance required.

3. AI Speeds Up Testing

Creating and maintaining test coverage is time-consuming, even with traditional low-code tools. At the same time, engineering teams are shipping faster than ever, often using AI to generate production code at high velocity. This increases the risk of testing becoming a bottleneck.

AI can also accelerate quality. An autonomous testing agent like Autify Aximo expands coverage by executing real user workflows through natural language and visual understanding. Instead of manually building and maintaining scripts, teams can describe the flows they want validated, and Aximo plans and executes them end to end.

Because Aximo operates at the user level rather than relying on static locators, it adapts to UI changes and dynamic interfaces without constant rewrites. This allows teams to validate more scenarios, more frequently, without proportionally increasing maintenance effort.

If expanding test coverage without expanding overhead is a priority, try Autify Aximo and see how it fits into your workflow.

Conclusion

In this post, you’ve seen how important it is to increase test coverage for your applications. You’ve also seen that there are many techniques you can leverage. 

Nowadays, AI has become such a valuable tool that you simply can’t afford not to use it. That’s true when it comes to creating software, of course, but you can and should also use AI for testing and quality assurance.