Lasse Foo-Rafn

Posted on Oct 23

From Good to Great: Taking Your Test Coverage to the Next Level

#javascript #beginners #cicd #devops

Want to boost your test coverage? Here's what you need to know:

Test coverage measures how much of your code is tested
It's not about hitting 100%, but testing what matters most
Mix different test types for comprehensive coverage
Use tools to track and improve coverage
Keep tests up-to-date as your code changes

Key strategies to improve test coverage:

Focus on core business logic and high-risk areas
Use a mix of unit, integration, and end-to-end tests
Make coverage part of code reviews
Update tests regularly with code changes
Use AI and automation to create and maintain tests
Consider community testing for diverse perspectives

Popular test coverage tools:

Tool	Language	Key Feature
SonarQube	Multiple	Finds bugs and security issues
Codecov	Multiple	PR comments and insights
Simplecov	Ruby	Coverage reports
JaCoCo	Java	Maven/Gradle integration
Istanbul	JavaScript	Unit and integration test coverage

Remember: Good test coverage isn't just about numbers—it's about writing meaningful tests that catch real issues and improve code quality.

Test Coverage Metrics

Test coverage metrics show how well your tests cover your code. Let's look at the main types.

Code Coverage

Code coverage is the big picture. It's the percentage of your code that your tests hit.

Code Coverage = (Tested Code Lines / Total Code Lines) x 100

Example: 800 tested lines out of 1000 total = 80% code coverage.

Branch Coverage

Branch coverage checks if your tests hit all possible paths in your code. Take this function:

def check_number(num):
    if num > 0:
        return "Positive"
    elif num < 0:
        return "Negative"
    else:
        return "Zero"

For 100% branch coverage, you'd test with a positive number, a negative number, and zero.

Function Coverage

This one's simple: it's the percentage of your functions that get called during testing.

Statement Coverage

Statement coverage looks at individual code statements. It's more detailed than line coverage.

Comparing Coverage Metrics

Here's how these metrics stack up:

Metric	Measures	Pros	Cons
Code Coverage	Overall code tested	Easy to grasp	Might miss complex logic
Branch Coverage	Decision paths tested	Finds logic gaps	Hard to hit 100%
Function Coverage	Functions called	Simple to use	Misses function internals
Statement Coverage	Statements executed	More detailed	Time-consuming to improve

Each metric has its use. Code coverage gives a quick overview, branch coverage digs into logic, function coverage ensures you're hitting all parts, and statement coverage gets into details.

Advanced Test Coverage Methods

Let's dive into four powerful ways to boost your test coverage for tricky scenarios:

Risk-Based Testing

Risk-based testing (RBT) zeroes in on the parts of your app most likely to break. It's perfect when you're short on time or resources.

How to do RBT:

1. Spot the risky areas in your app

2. Rank these risks

3. Test based on risk priority

4. Re-evaluate after each sprint

Think about an e-commerce app. You'd test the payment system way more than a minor UI tweak, right?

Mutation Testing

Mutation testing is like a stress test for your tests. It tweaks your code slightly and sees if your tests catch it.

Here's a quick example:

# Normal code
def add(a, b):
    return a + b

# Mutated code
def add(a, b):
    return a - b  # Swapped + for -

If your tests don't fail after this change, your test suite needs work.

Property-Based Testing

Instead of writing specific test cases, property-based testing checks if your code behaves correctly for a wide range of inputs.

Here's how it looks using fast-check in JavaScript:

const fc = require('fast-check');

fc.assert(
  fc.property(fc.nat(), fc.nat(), (a, b) => {
    const sum = add(a, b);
    return sum > a && sum > b;
  })
);

This test throws random numbers at your add function to make sure it always works right.

Fuzzing

Fuzzing bombards your app with random, weird inputs to find weak spots. It's great for uncovering security issues.

You might use a fuzzing tool to hit a form with thousands of random inputs, looking for crashes or odd behavior.

Method	Best For	Main Perk
Risk-Based Testing	Big projects, tight deadlines	Tackles the important stuff first
Mutation Testing	Beefing up your test suite	Finds holes in your tests
Property-Based Testing	Tricky algorithms, data structures	Makes sure your code can handle anything
Fuzzing	Security checks, input validation	Uncovers sneaky vulnerabilities

Improving Test Coverage

Let's look at three ways to boost your test coverage and make testing more efficient.

Parallel Testing

Parallel testing runs multiple tests at once instead of one after another. It's a game-changer because:

You get results faster
You can run more tests in less time
Your hardware works harder

Here's a real example:

"We run unit tests on every commit, integration tests on repo changes, and automated functional tests before deployment. Load tests happen before official releases."

This approach tests thoroughly at different stages without slowing things down.

Test Speed and Efficiency

Slow tests can hold up your whole process. Here's how to speed up:

1. Find slow tests

Use tools to spot tests that take too long.

2. Fix or split slow tests

Make slow tests faster or break them into smaller parts.

3. Run fast tests first

This helps you catch problems early and saves time overall.

Test Type	How to Speed It Up
Unit Tests	Keep them small and focused
Integration Tests	Mock external parts
UI Tests	Use headless browsers

Using Continuous Integration

Continuous Integration (CI) helps keep test coverage high as you develop. Here's how to set it up:

1. Pick a CI tool

Options include Jenkins, GitLab CI, or CircleCI.

2. Automate your tests

Make your CI pipeline run tests automatically when code changes.

3. Track coverage

Use tools like OtterWise to see how coverage changes over time.

CI isn't just about running tests. It's about keeping everyone in the loop about your code's health.

"A fast CI/CD pipeline provides a 41 to 1 return on investment."

This shows why it's worth spending time on a good CI system.

Tools for Better Coverage

Let's check out some tools to boost your test coverage and make your code more solid.

Common Test Coverage Tools

Here are some popular tools for tracking and improving test coverage:

Tool	Language	What It Does
SonarQube	Multiple	Finds bugs, code smells, security issues
Codecov	Multiple	Comments on PRs, gives coverage insights
Simplecov	Ruby	Creates test coverage reports
DeepScan	JavaScript/TypeScript	Spots null reference errors, memory leaks
Bandit	Python	Identifies security problems

SonarQube is a standout. It measures code coverage and gives you the lowdown on code health. Plus, it plays nice with GitHub Actions and GitLab CI/CD.

OtterWise for Code Quality

OtterWise offers:

Code coverage tracking
Quality metrics reporting
CI/CD pipeline integration

To get the most out of OtterWise (or similar tools):

Set up auto-analysis on code commits
Use quality gates to block low-quality code
Let the reports guide your testing

Automatic Coverage Reports in CI/CD

Want quick insights into your code's health? Integrate coverage reports into your CI/CD pipeline. Here's how:

Pick a coverage tool that fits your stack
Set up your CI/CD to run tests and make coverage reports
Auto-post results to a service like Coveralls

Here's a CircleCI and Coveralls example:

version: 2.1
orbs:
  coveralls: coveralls/coveralls@1.0.6
jobs:
  build:
    docker:
      - image: cimg/ruby:3.0.0
    steps:
      - checkout
      - run: bundle install
      - run: bundle exec rspec
      - coveralls/upload

This setup runs tests, makes a coverage report, and sends it to Coveralls automatically.

"A fast CI/CD pipeline provides a 41 to 1 return on investment."

Best Practices for Test Coverage

Want to boost your test coverage? Here's how:

Focus on Key Code Areas

Don't chase 100% coverage. Instead, zero in on:

Core business logic
High-traffic user flows
Bug-prone areas

Spotify, for example, heavily tests their recommendation algorithm and playlist generation features.

Mix Different Test Types

Balance your test suite:

Test Type	Purpose	Coverage Level
Unit	Individual functions/methods	High
Integration	Component interactions	Medium
End-to-end	Complete user flows	Low

Aim for a pyramid: lots of unit tests, fewer integration tests, and a handful of end-to-end tests.

Include Coverage in Code Reviews

Make test coverage part of your code review process. It catches gaps early and promotes a testing culture.

Some teams use tools like Codecov to automatically comment on pull requests with coverage info.

Update Tests Regularly

Your tests should evolve with your code. Each sprint, set aside time to:

Remove obsolete tests
Add tests for new features
Refactor tests to match code changes

Netflix has a dedicated "Test Engineering" team for this.

Remember, it's not about high numbers, but meaningful tests that catch real issues. As Ann from SonarSource says:

"Ignore overall coverage and enforce that all New Code has 80% coverage. Gradually - and this was our own experience internally - overall coverage will naturally increase."

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Common Test Coverage Challenges

Testing isn't always easy. Here are some hurdles you might face:

Testing Old Code

Legacy code can be a pain to test. It often lacks docs and has tightly coupled components.

To tackle this:

Start small: Pick a key module and test it
Use characterization tests: Capture current behavior
Refactor bit by bit: Break down big functions

A financial company did this with their old Java system. They:

Split code into smaller functions
Wrote tests for each bug fix
Switched to JUnit 5

Result? A more reliable system with more automated tests.

Managing Test Data

Bad test data = unreliable tests. Here's what to do:

Use fixtures or factories for consistent test environments
Keep test and production data separate
Refresh test data often

Testing Async Code

Async code can cause timing issues. Try these:

Use callbacks or Promises
Implement polling
Use async-specific testing frameworks

Fixing Unreliable Tests

Flaky tests are a problem. Google found 41% of their tests were flaky, Microsoft 26%.

To fix them:

Find the culprits: Track test reliability
Isolate tests: No dependencies on other tests
Make tests hermetic: Consistent results, no matter what

Pro tip: Fix flaky tests ASAP. Document, create a ticket, and tackle it.

"Code without tests is bad code. It doesn't matter how well written it is; it doesn't matter how pretty or object-oriented or well-encapsulated it is. With tests, we can change the behavior of our code quickly and verifiably. Without them, we really don't know if our code is getting better or worse." - Michael C. Feathers, Author of "Working Effectively with Legacy Code"

More Ways to Improve Coverage

Behavior-Driven Development

BDD brings testing closer to business needs. It uses plain language to describe how software should work, making it easier for everyone to understand.

Here's what BDD does:

Gets the team talking
Starts testing early
Cuts down on confusion

BDD gets Business Analysts, Developers, and QA Teams chatting regularly. This helps make sure tests cover all the important stuff.

Take a travel booking app. A BDD scenario might look like this:

Given a user searches for flights
When they select a flight
Then they should see the booking details

This clearly shows what should happen, making it a breeze to create solid tests.

AI in Testing

AI is shaking up software testing. It helps create and maintain test cases, saving time and boosting coverage.

AI Testing Perks	Numbers
More Coverage	Up to 85% boost
Lower Costs	30% drop
Better Efficiency	25% increase

Here's a real example:

For UI testing, AI tools led to:

80% faster test case creation
40% more edge cases covered

The team used ChatGPT to whip up test cases from requirements when time was tight.

Community Testing

Community testing (or crowd testing) taps into a diverse group of testers. They often spot things in-house teams miss.

Why it's great:

Lots of devices and setups tested
Fresh eyes from real users
Saves money while improving quality

InnoTech and Cleanwatts teamed up to make the Kiome app better. They got 120 real users involved, which was key to improving the app.

Fidelidade also picked InnoTech for a two-year crowd testing project on their Drive app. They wanted to make the app's services, quality, performance, and user experience better.

Community testing is booming. It's set to hit $2.5 billion by 2027, growing 12.9% each year from 2023 to 2027.

"The real world matters, and the differences are just ballooning and you just cannot keep up, even as a large company in the lab." - Rob Mason, Applause

Tracking Test Coverage Results

Setting Realistic Goals

Don't aim for 100% coverage. It's not practical. Instead, set goals that make sense for your team.

Google's coverage guidelines:

Coverage Level	Percentage
Acceptable	60%
Commendable	75%
Exemplary	90%

Many teams find 80% coverage works well. It's thorough without wasting time on rare edge cases.

Reading Coverage Reports

When you look at coverage reports, focus on:

'E' (else path not taken) or 'I' (if path not taken) marks
How many times each line ran (shown as xN)
Color highlights:
- Red: Not run
- Pink: Statements missed
- Orange: Functions missed
- Yellow: Branches missed

Use these to spot testing gaps and improve your coverage.

Using Metrics in Development

Track coverage metrics regularly. Here's how:

1. Set up automated reporting

Add coverage reporting to your CI/CD pipeline. With Jest, it's as simple as:

npm test -- --coverage --coverageDirectory='coverage'

This creates an HTML report in the 'coverage' folder after each test run.

2. Focus on what matters

Use tools like Codecov's file viewer to find poorly covered areas. Prioritize testing:

Core features
High-risk code
Often-changed parts

3. Watch for changes

Keep an eye on coverage trends. If it drops below your target, find out why and fix it.

4. Use different metrics

Don't just look at line coverage. Consider these too:

Metric	What it Checks
Function Coverage	Functions called in tests
Statement Coverage	Statements run
Branch Coverage	Decision paths explored
Condition Coverage	Boolean outcomes tested

Looking at various metrics gives you a better picture of your test coverage.

Future of Test Coverage

The test coverage landscape is changing fast. New tech is shaking things up. Let's look at what's coming:

AI-Created Tests

AI is already making a splash in testing. Take Diffblue, for example. They've built AI that writes Java unit tests. It can pump out entire test suites on its own.

And it's not just a gimmick. Capgemini's 2023 report shows companies using AI in testing cut testing time by 30% and caught 25% more bugs.

Here's what AI brings to the table:

AI Skill	Testing Impact
Test Creation	Builds full test suites automatically
Pattern Spotting	Finds areas that need more tests
Future Guessing	Suggests tests for potential issues

Predicting Coverage Gaps

AI isn't just writing tests. It's pointing out where we need them most. New tools use machine learning to scan code and guess where bugs might pop up.

Microsoft's Code Defect AI is a prime example. It digs through past data to flag potential problems. This helps teams zero in on the riskiest areas.

Quantum Computing and Testing

Quantum computing is about to flip testing on its head. It's early days, but it's already showing promise for speeding up certain tests.

But here's the catch: we need new ways to check quantum algorithms. The old methods just won't work.

Dr. John Preskill from Caltech puts it this way:

"Checking quantum software is a whole new ballgame. We're not just hunting for regular bugs. We need to make sure our quantum operations actually work."

This means QA teams will need to level up their skills and come up with quantum-specific testing tricks.

Looking ahead, test coverage is likely to become:

More automated: AI will handle the grunt work
More predictive: Tools will spot issues before they happen
More specialized: New tech like quantum will need custom testing approaches

The future of test coverage isn't about working harder. It's about working smarter. By jumping on these new technologies, teams can boost their coverage without burning out.

Conclusion

Test coverage isn't just about hitting a number. It's about making sure your code actually works. Here's the deal:

Mix up your test types
Focus on the important stuff first
Keep your tests up-to-date
Make it a team thing

Want to get better at test coverage? Here's how:

1. Set clear goals

Start small. SonarSource says aim for 80% coverage on new code. It's a smart way to improve without getting bogged down in old stuff.

2. Use the right tools

Pick what works for you. Java? Try Pitest for mutation testing. JavaScript? Stryker Mutator's got you covered.

3. Learn from others

Capgemini's 2023 report showed AI in testing cut time by 30% and caught 25% more bugs. That's worth looking into.

4. Keep up with trends

Shift-left testing is big now. It's all about testing earlier to catch problems sooner.

5. Mix it up

Don't just rely on automated tests. A combo of automated and manual gives you better coverage.

Test Type	Why It's Good
Automated	Fast, repeatable
Manual	Catches tricky issues
Shift-left	Finds bugs early
AI-assisted	More efficient

FAQs

How do you improve test coverage?