What Is Code Coverage and How to Optimize It?

Introduction

Imagine a world where software never crashes, where users never encounter bugs, and where everything just works. Sounds like a dream, right? Well, measuring code coverage can bring us closer to this ideal by helping us ensure that the code is thoroughly tested and ready for the real world. 

In this article, we'll explore the benefits of measuring code coverage and how it can help us achieve greater confidence in the quality of our software.

What is code coverage?

Code coverage metric is a measurement that tells us how much of our source code is covered by automated tests. It provides an indication of the quality of the tests and the likelihood of finding bugs. A higher code coverage means more code is tested, which increases our confidence in the software.

The metric helps us identify areas of our codebase that have not been exercised by our tests and can help us improve the quality of our code by uncovering potential bugs and issues. It can also serve as a useful tool for demonstrating the thoroughness of our testing efforts to stakeholders and customers.

On the other hand, measuring code coverage also brings risks associated with relying too heavily on this metric, false sense of security, and overemphasizing quantity over quality.

Benefits of measuring code coverage

Helps us identify untested code

Measuring code coverage ensures that all parts of the code are tested. It helps us identify untested code, which we can then focus on to improve our tests.

Improves code quality

Code quality improves since we are able to identify areas of the code that are complex or difficult to test. By improving the test coverage of these areas, we can improve the overall quality of it.

Reduces the risk of creating bugs

Code coverage helps us find bugs early in the development cycle. The earlier we find bugs, the easier and less costly they are to fix.

Saves time and resources

Measuring code coverage helps us prioritize our testing efforts. It ensures that we are focusing on the most critical parts of the code, which saves time and resources.

Increases customer satisfaction

A well-tested codebase results in a more stable and reliable software product. This, in turn, leads to higher customer satisfaction and loyalty.

Risks connected to measuring code coverage

False sense of security

High code coverage does not necessarily mean that the code is bug-free. There may still be defects in untested areas, leading to a false sense of security.

Overemphasizing quantity over quality

Focusing too much on achieving high code coverage numbers may lead to writing tests for the sake of coverage instead of focusing on test quality and effectiveness.

Limited scope of coverage

Code coverage metrics only measure the number of lines or branches of code that were executed during testing. This metric does not indicate if the tests are comprehensive enough to catch all potential issues.

Inaccurate measurement

Depending on the tool used to measure code coverage, there may be inaccuracies or limitations that affect the quality of the data collected.

Time-consuming and costly

Measuring code coverage can be time-consuming and expensive, particularly in larger projects. This can divert resources and slow down the development process if not managed effectively.

Risks of relying solely on code coverage

Relying solely on code coverage can lead to inaccurate insights and misguided decisions. High code coverage doesn't necessarily mean good code quality. Here are some examples:

• Code coverage may focus on measuring the quantity of tests, rather than their quality. It's possible to have a high percentage of code coverage with ineffective tests that don't catch bugs or ensure proper functionality.

• Code coverage may incentivize developers to write tests that merely exercise code paths, without truly testing the behavior of the system. This can result in false positives and misleading results.
• Code coverage doesn't necessarily take into account the quality of the tests themselves. Poorly designed or poorly implemented tests can produce inaccurate results, even if they achieve high coverage.
• Code coverage can give a false sense of security. It's possible to have high code coverage, but still have bugs and issues in the code that the tests fail to catch.
• Code coverage may lead to neglecting parts of the codebase that aren't being exercised by the tests. This can result in important functionality going untested and overlooked.

How to measure code coverage?

To measure code coverage, we need to use specialized code coverage tools that can track which lines of code are executed during testing. These tools help us identify gaps in our test suite, so we can add more tests to cover the uncovered code. One popular code coverage tool is JaCoCo, which integrates with build systems like Maven and Gradle.

For example, if we have a Java application, we can configure JaCoCo in our build script to generate a code coverage report after each test run. The report shows us the percentage of code that was covered by the tests, broken down by class, method, and line.

In addition to using code coverage tools, we can also use code quality platforms like SonarQube to monitor code coverage and other code quality metrics over time. This allows us to identify trends and catch regressions early.

Example for a JavaScript application

One way to measure code coverage in a JavaScript application is by using a tool like NYC Istanbul. NYC is a code coverage tool that can be used with popular testing frameworks such as Mocha, Jasmine, and Jest.

First, you would need to install NYC as a dependency in your project using a package manager like npm. Then, you can configure it to generate a coverage report by running your test suite with Istanbul's command-line interface.

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For example, let's say you have a JavaScript function “add” that adds two numbers and you want to measure its code coverage. Here's how you could do it using Mocha and NYC:

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1. Install Mocha and NYC as dev dependencies:

npm install --save-dev mocha nyc

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2. Add test script in project’s package. Json:

{

"scripts": {

"test": "nyc mocha"

}

}

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3. Run the test suite with npm run test

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This will generate a coverage report in HTML format under the ./coverage directory. You can open the index.html file in a web browser to see the coverage report, which shows which lines of code were executed during the test and which lines were not.

Code coverage alternatives

There are several alternatives to code coverage metrics that can help teams measure software quality:

Number of post-release bugs found

It’s a metric that measures the number of defects or issues discovered in a software product after it has been released to customers or users. By tracking the number of post-release bugs found, we can identify areas for improvement in our testing processes and address them to prevent future issues.

Mean Time to Repair

MTTR (Mean Time To Repair) is a metric in software quality that measures the average time taken to resolve a system failure or defect. It is an important metric as it provides insight into how quickly an organization can respond to and resolve issues in their software products. By tracking MTTR, we can identify areas for improvement in our software development and deployment processes and work to optimize them to reduce the time taken to resolve issues.

Mean Time to Failure

MTTF (Mean Time To Failure) is a metric in software quality that measures the average time between system failures or defects. It is an important metric as it provides insight into the reliability and stability of a software product. By tracking MTTF, we can identify areas of the software that are prone to failure or defects, and work to improve them to increase the reliability and stability of the product.

Mutation Testing

Mutation testing involves introducing faults (mutations) into the code to see if the tests can detect them. It provides a more thorough way of measuring test quality than code coverage because it tests the ability of tests to detect specific faults. Mutation testing is more complex and resource-intensive than code coverage, so it's better suited for critical systems where thorough testing is essential.

Static Code Analysis

Static code analysis involves analyzing the code without executing it, looking for defects and vulnerabilities. It can identify issues that may not be caught by tests, such as potential security vulnerabilities or unused code. Nowadays, almost all projects and frameworks use static code analysis, when it comes to JS/TS, it's usually done with `eslint`.

User Feedback

Net Promoter Score (NPS) is a widely used customer satisfaction metric that measures how likely users are to recommend a product or service to others. User feedback can provide valuable insight into the user experience and highlight areas where the product may need improvement. While not a direct measure of software quality, NPS can be a useful complement to code coverage and other metrics.

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When to choose each option:

• Use code coverage metrics to ensure that tests are covering a sufficient portion of the codebase, particularly for critical systems or those with strict regulatory requirements.
• Use mutation testing when thorough testing is essential and you need to ensure that tests are detecting specific faults in the code.
• Use static code analysis when you need to catch issues that may not be caught by manual code reviews. The current cost of using automated static code analysis is relatively low but we can avoid a lot of issues with it.
• Use NPS or other user feedback metrics to gain insight into the user experience and identify areas where the product may need improvement.

Summary

With code coverage metrics, teams can identify areas of their codebase that require more testing and measure the effectiveness of their testing efforts. By doing so, they can improve the quality of their software, resulting in increased customer satisfaction, reduced maintenance costs, and faster time-to-market.

With the right metrics in place, you can identify areas for improvement, track progress, and ensure that your software meets the needs of your users. Dive into the world of software quality metrics and choose the right ones for your product. Your users (and your team) will thank you for it.