As a developer, you‘ve likely experienced the frustration of unexpected bugs cropping up in your code after you‘ve deployed it to production. These defects – whether caused by logical errors, unexpected user input, or something else entirely – can negatively impact your users and damage your product‘s reputation.
The goal of software testing is to identify these issues before they affect real customers. And code coverage gives you an invaluable metric for measuring the comprehensiveness and thoroughness of your tests.
Code coverage tells you what percentage of your codebase is actually executed when you run your test suite. The higher the coverage, the more confident you can feel that your testing is exercising a wide range of use cases and not missing any dark corners of your application where bugs could be lurking unnoticed.
But what exactly does code coverage measure and how can you use it effectively? Let‘s decode this essential software testing tool.
Why Should You Care About Code Coverage?
Before diving into the details, it‘s worth stepping back and looking at why code coverage matters in the first place.
According to data from the Consortium for IT Software Quality, software bugs cost the US economy an estimated $59.5 billion annually. Furthermore, over 50% of application defects are found by customers, not developers.
Clearly, improving the quality and coverage of testing is critical to reducing expensive software failures down the line.
As a report from Atlassian notes, higher code coverage directly corresponds with reduced production defects:
| Code Coverage | Defects per KSLOC |
|---|---|
| 0-50% | 0.5 – 5.5 |
| 50-80% | 0.1 – 0.5 |
| 80-100% | 0.05 – 0.1 |
Table showing correlation between higher code coverage and fewer defects
Or to put it visually:
It‘s no surprise that many regulated industries actually mandate extremely high code coverage to meet quality standards. For example, the FAA requires 100% coverage for avionics software, and anything deployed to space via NASA requires 80% minimum branch coverage.
Clearly, maximizing code coverage provides tremendous business value by catching bugs before software makes it to real users. The key is understanding exactly how to measure it.
Code Coverage Metrics: What Are You Measuring?
There are a number of different code coverage metrics to be aware of, each providing a different perspective on how thoroughly your tests exercise your application code:
- Function coverage – What percentage of defined functions/methods in the code were called during testing?
- Statement coverage – What percentage of executable statements were executed by tests?
- Branch coverage – What percentage of decision points evaluated both the true and false paths?
- Line coverage – What lines of code were executed during testing?
- Path coverage – What percentage of all possible paths through the code were tested?
Branch coverage is generally considered one of the most important coverage metrics, as it measures whether all logical paths have been evaluated.
For example, consider the following simple Python function:
def my_function(x):
if x > 5:
return ‘Greater than 5‘
if x < 0:
return ‘Negative number‘
return ‘Between 0 and 5‘ 100% statement coverage would require calling my_function() three times with different arguments to execute all statements.
But for 100% branch coverage, you would need five test cases to cover every logical path:
my_function(6)my_function(3)my_function(-1)my_function(0)my_function(None)– raises exception
By measuring branch coverage in addition to statement coverage, you can confirm that your tests are accounting for edge cases and not just blindly executing lines of code.
Best Practices for Effective Code Coverage
How do you ensure your test suite provides maximum code coverage to catch bugs efficiently? Follow these tips:
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Set coverage goals: Establish target coverage percentages for different metrics – e.g. 70% statement coverage and 50% branch coverage. This focuses testing efforts.
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Prioritize complex areas: Even with high overall coverage, double check coverage in complex areas like validators or exception handling.
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Incremental enforcement: For large codebases, enforce 90%+ coverage per commit rather than blanket coverage.
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Analyze results: Use coverage reports to find low coverage areas and optimize tests accordingly.
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Focus on quality: Don‘t aim solely for 100% coverage – emphasize writing meaningful, high value tests.
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Combine test types: Aggregate unit, integration, and system test results to confirm coverage across testing levels.
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Re-check periodically: Rerun coverage periodically to confirm changes aren‘t reducing coverage over time.
Following best practices helps ensure your test suite provides comprehensive code coverage as an effective safety net.
How Code Coverage Tools Work
There are a wide variety of open source code coverage tools available to integrate coverage metrics into your workflow. While the implementation details vary, most operate on the same basic principles:
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Instrumentation: The tool inserts tracking code at important points like function calls or branch points.
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Execution: Your test suite runs, exercising different application code paths.
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Aggregation: The tool aggregates execution data to determine coverage percentages.
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Reporting: HTML, XML or JSON reports visualize covered/uncovered code areas.
This straightforward approach provides detailed insights without being disruptive or difficult to adopt.
Top Code Coverage Tools By Language
With the fundamentals covered, let‘s survey some of the top open source coverage tools used across major languages:
Java
Java developers often turn to JaCoCo, which provides statement, branch and complexity coverage metrics along with IDE integration for instant feedback. Clover is another popular commercial option developed by Atlassian, offering historical coverage trend reports.
JavaScript
For JavaScript projects, Istanbul has become a standard choice. It highlights uncovered code right in your editor and can handle modern ES6+ features. Jest is another option with built-in coverage reports.
Python
Coverage.py is the go-to for Python coverage, offering branch coverage support, XML/HTML output and integration with testing tools like pytest. Nose is another widely used coverage library.
Ruby
SimpleCov is a powerful Ruby coverage tool that produces detailed reports with color-coded source files to distinguish covered vs. uncovered code. DeepCove takes a stricter approach for more accurate coverage percentages.
C#/.NET
In the .NET ecosystem, Coverlet provides highly customizable coverage analysis integrating with .NET Core test frameworks. OpenCover is an extensible tool that highlights uncovered code areas.
PHP
For PHP developers, PHPUnit and its visual code coverage included reports offer zero-configuration code coverage support. PCOV provides statement, branch and cyclomatic complexity metrics.
There are also many cloud-based services like Codecov that provide coverage reporting and analytics across multiple languages in CI/CD pipelines.
Key Takeaways
Getting high code coverage takes work – but it pays dividends in terms of shipping high quality, thoroughly tested software that won‘t let your users down. By understanding what coverage metrics reveal about your tests, following best practices, and leveraging available tools, you can make coverage an ally rather than an obstacle.
The bottom line is that excellent code coverage gives you confidence that your testing has explored your application behavior extensively before you unleash it on the world. And eliminating nasty surprises is something every developer can appreciate!
