If you’ve spent any time in software testing, you’ve probably heard this mantra: “Strive for 100% test coverage” or something similar.
On paper, it sounds great: a perfectly tested codebase, free from bugs and flaws, but does achieving 100% test coverage guarantee high-quality software?
Unfortunately, the answer is NO.
In this article, we’ll explore why adding more tests doesn't always improve quality. We'll also examine why chasing perfect coverage can often be counterproductive. Finally, I’ll provide smarter ways to improve software quality. But first, let's review the basics.
Test coverage is a metric that indicates the percentage of your codebase that is tested.
For example, if your tests execute 80% of your code, you have 80% test coverage. This shows which parts of your code have been tested and which remain untested.
There are different ways to measure test coverage, such as line coverage, branch, statement, and function coverage, and while these metrics sound helpful, they only measure the amount of code tested. They don’t measure the quality or effectiveness of the tests. That is, while you may have 100% coverage, this does not mean that your software is flawless.
So, why do many teams still push for test coverage?
Many development teams prioritize test coverage as a quality metric because it provides an organized way to measure software quality. It's often seen as an effective benchmark for code reliability for several reasons.
It's cheaper and easier to catch and fix bugs during development than during production.
A high test coverage suggests that most of the code has been executed at least once, increasing the likelihood of identifying issues early. This can help prevent errors like null reference errors and unhandled exceptions before they affect users.
High test coverage enables developers to make changes confidently, knowing that automated tests will catch any regression. Frequent updates, feature additions, and refactoring are common in software development, and without proper test coverage, even a small change in the codebase can affect another part of the application.
This is important, especially when working with a large codebase, where manually verifying every component is impractical.
High test coverage is a legal necessity in industries that handle sensitive data, such as healthcare, finance, and aviation. These sectors must meet strict regulatory standards for quality, reliability, and security. Regulators in these industries sometimes demand proof that all functionalities have been thoroughly tested.
For example, in healthcare, the FDA (U.S. Food and Drug Administration) requires software used in medical devices to undergo extensive testing before approval.
Test coverage serves as a quality gate in modern development pipelines. Many teams use Continuous Integration/Continuous Deployment (CI/CD) pipelines to automate testing and deployment and automatically prevent untested code from reaching production.
This ensures that the software remains stable in rapidly changing development environments.
These explain why high test coverage is pushed in many development teams. However, while striving for better test coverage can be helpful, the aggressive pursuit of 100% coverage can create unexpected problems.
Test coverage only measures how much of the code has been tested. It does not indicate whether the right things have been tested, tested well, or whether the tests are meaningful.
There are some misconceptions about high test coverage, like assuming it guarantees bug-free software. But it’s not just about the misconceptions. Blindly, chasing 100% test coverage can create more problems than it solves. Here’s how:
1. False sense of security
Hitting 100% might feel like a win, but there might be underlying issues. Bugs often hide in areas that automated tests cannot predict, such as untested edge cases or real-world scenarios, because coverage doesn’t reflect real-world scenarios or complex workflows.
2. Diminishing returns
As you get closer to 100%, it becomes harder to add meaningful tests. At some point, you’re just writing shallow, redundant tests to tick a box or boost numbers. You may even start writing tests that cover lines of code but don’t validate real functionality.
For example, suppose you have a function that returns a constant value:
function getAppVersion() {
return "1.0.0";
}
You could write a test to check if this function returns "1.0.0." However, this does not improve software quality because the function has no logic, and the test doesn’t add any real value. It's only there to increase coverage numbers.
3. Missed scenarios
Even with full coverage, critical bugs can slip through in edge cases, performance under load, user input variations, and complex workflows.
So, if 100% test coverage isn’t the answer, what is?
The solution is to focus on the quality of your tests rather than their quantity.
Don't just rely on test coverage to measure quality. Instead, adopt a multi-metric approach that takes into account not only how much code is tested but also how effectively the software satisfies user needs and performs in real-world scenarios.
Here are some important metrics that can be combined to provide a more accurate view of your software's quality:
1. Defect density
This measures the number of bugs found in a given amount of code (e.g., defects per 100 lines of code). For example, if a codebase has 50 defects in 10,000 lines of code, the defect density would be 5 defects per thousands of lines of code (KLOC).
A high test coverage percentage with a high defect density indicates that your tests might be missing critical paths or too shallow to catch real issues.
This metric can help assess how effective the test is in catching bugs.
2. Code complexity
Code complexity indicates how difficult it is to test and maintain a piece of code. The more complex your code is, the more likely it is to hide bugs, requiring targeted testing to detect those hidden bugs.
For example, a complex function might have multiple conditions for different types of loans:
function calculateInterest(principal, rate, time, loanType) {
if (loanType === "business") {
rate += 0.5;
} else if (loanType === "student") {
rate += 0.2;
} else if (loanType === "mortgage" && time > 10) {
rate += 0.3;
}
return (principal * rate * time) / 100;
}
Consider combining code complexity and test coverage to help identify high-risk areas in your codebase. Modules with complex functions and low test coverage indicate a high-risk area for hidden bugs.
3. Code churn
Code churn is how often your code changes. It indicates how maintainable your codebase is.
It matters because sometimes frequent changes to the same code suggest instability, and taking too long to fix it can indicate inefficient testing strategies.
For example, if a login logic is changed weekly due to security updates or bug fixes, that’s high churn. It may indicate poor initial design or rushed releases.
Monitor code churn and test coverage to identify weak areas of the codebase.
4. Test effectiveness and User Acceptance Test (UAT)
Test effectiveness measures how well your tests detect bugs, while User Acceptance Testing (UAT) assesses whether the software meets user needs. Both metrics are important because software quality is ultimately determined by real-world usage.
For example, in your ride-sharing app, your tests cover areas like user registration, booking a ride, and processing payment, but you rarely find bugs in production. But then, users start reporting that the app crashes when selecting a destination. This suggests your test cases might not reflect real-world scenarios.
You can track defect detection rates by logging defects found by both tests and users using tools like Jira, Bugzilla, or Azure DevOps. Then, you can compare the number of defects caught in testing to those reported post-release, adjusting your test strategy to focus on high-impact areas and improving overall software reliability.
Chasing 100% test coverage alone won’t guarantee software quality. Instead, focus on combining multiple metrics to get a clearer view of where there are quality risks and fix them to build high-quality, reliable software.
Instead of blindly chasing high test coverage, focus on the quality, relevance, and effectiveness of your tests. Prioritize real-world scenarios, critical paths, and risk-based testing. Great software is defined by how well it serves users, not by perfect metrics.
What do you think? Do you still see 100% test coverage as the ultimate goal, or are you ready to adopt a smarter, quality-driven approach to testing?