Comprehensive Guide on Test Coverage: All You Need to Learn for Testing Software

Test coverage refers to the amount of code, functional elements, risks, and other testable items that are verified by testing. It gives you a high-level measurement of test thoroughness.

Here are some examples of what test coverage measures:

• Percentage of code executed by automated tests
• Proportion of user stories validated against acceptance criteria
• Extent of edge cases and boundary values tested
• Number of browser/device combinations covered
• Portion of identified risks mitigated via testing

The key point is that test coverage examines the breadth and depth of testing from a holistic perspective. It goes beyond code coverage alone to incorporate other test types like manual, integration, and user acceptance testing.

The goal is to make sure every part of the application is tested appropriately based on its priority and risk level. Of course, achieving 100% test coverage is unrealistic for any sizable system. Instead, optimize coverage based on your resources, timeline, and quality goals.

Thorough testing is crucial for releasing high-quality software that delights users. But how can you objectively evaluate testing completeness? That’s where measuring coverage comes in handy.

Here are some key benefits of tracking test coverage:

Provides Visibility

The first and most obvious advantage is visibility. Test coverage metrics shine a light on any gaps in your validation activities.

For example, you might discover an important user workflow that lacks automated UI tests. Or realize that negative test cases are neglected. Making these testing blind spots visible is the essential first step toward improving coverage.

Enables Optimization

With test coverage data, you can make strategic decisions about allocating test resources. Focus on parts of the system with insufficient coverage first.

You can also quantify the impact of testing process changes. For instance, does integrating security scanning into the CI pipeline improve risk coverage? Test coverage metrics contain these insights.

Demonstrates Completeness

Test coverage also demonstrates completeness against requirements. For user stories, what percentage have corresponding acceptance tests? What proportion of documented risks are mitigated?

These questions help assess test suite quality and identify missing tests. Such metrics provide confidence that you have sufficiently validated the application before release.

Supports Reports & Analysis

Quantitative coverage data enables all kinds of useful reports and analysis. Test coverage trends over releases highlight improvements and regressions. Drilling down, you can compare modules with high vs. low coverage.

Such reporting and analytics aid essential test optimization activities. You can target problematic areas and celebrate successes with numbers to back it up.

Drives Collaboration

Finally, test coverage encourages collaboration between testers and developers. Code coverage clearly reveals whether new code is tested. Testers can request new unit tests for low coverage modules.

Similarly, functional gaps found during testing can inform new user stories for upcoming sprints. Making coverage metrics transparent fosters such positive engagement between teams.

Types of Test Coverage

Now that you appreciate the value of test coverage, let’s examine the types you might employ.

Coverage can be categorized several ways, including:

• By test level (unit, integration, system)
• By test type (manual, automated)
• By test objective (functional, risk, compatibility)

Grouping techniques this way helps match coverage metrics to stakeholders’ needs. For example, developers care about unit test coverage for new code, while managers want to know if user stories meet acceptance criteria.

Here are some common test coverage types:

Unit Test Coverage

Unit tests focus on exercising code in isolation—for instance, a single class method. Unit test coverage calculates the amount of code touched by these low-level tests.

Typical metrics include:

• Function coverage – Percentage of functions executed
• Statement coverage – Percentage of statements executed
• Branch coverage – Percentage of decision branches tested
• Modified condition/decision coverage (MC/DC) – All condition outcomes exercised

Such code-centric coverage metrics help developers assess test quality and completeness. They also support test-driven development (TDD).

Integration Test Coverage

Integration testing validates how components cooperate—for example, API interactions. Coverage considers the interfaces and scenarios exercised during integration testing.

Useful metrics include:

• API coverage – Percentage of APIs tested end-to-end
• Service coverage – Percentage of microservices tested together
• Protocol coverage – Percentage of protocol operations verified

These metrics focus on critical integration points to mitigate risk. Integration coverage also surfaces gaps like APIs lacking tests.

System Test Coverage

System testing involves validating the end-to-end system behavior under realistic conditions. Coverage examines the breadth of functionality, configurations, and use cases tested at this top-level.

System test coverage metrics can include:

• Requirement coverage – Planned vs. tested requirements
• User workflow coverage – Key workflows tested
• Configuration coverage – Platforms, browsers, devices tested
• Use case coverage – Extent of primary vs. secondary use cases tested

Such coverage demonstrates the system’s readiness for production deployment based on stakeholders’ expectations.

Manual Test Coverage

Even with extensive automation, manual exploratory testing provides value. Manual test coverage calculates the amount of business logic, UI flows, edge cases, and other items validated through hands-on testing.

Typical manual test coverage metrics include:

• Story coverage – User stories tested exploratorily
• Screen coverage – Percentage of screens tested
• Defect density – Defects found per KLOC
• Test case purpose achievement – Goals met by test cases

Manual testing metrics combined with automation coverage provide full test completeness visibility.

User Acceptance Test Coverage

Acceptance testing checks that the system meets users’ needs as described in requirements docs. Acceptance test coverage measures how thoroughly this validation occurs.

Useful UAT coverage metrics:

• Requirements covered – Planned vs. tested requirements
• Acceptance criteria covered – User stories meeting all criteria
• Story points covered – Fraction of estimated story points tested

These demonstrate stakeholder expectations are met before release. Low acceptance test coverage indicates insufficient user-oriented validation.

Risk Coverage

Every system has risks that can jeopardize its success if not mitigated. Risk coverage gauges the extent of validation performed against identified risks.

For example:

• Security risks tested – Percentage of OWASP Top 10 vulnerabilities evaluated
• Performance risks tested – Key load and stress scenarios covered
• Reliability risks tested – Resilience to infrastructure failures tested

Strong risk coverage provides confidence in the system’s robustness against real-world threats.

Compatibility Coverage

Most apps need to work across diverse platforms, browsers, and devices. Compatibility coverage measures how extensively you validate cross-environment operability.

Common compatibility coverage metrics:

• Browsers tested – Key browser types and versions
• Mobile devices tested – Mix of mobile devices against market share
• OS platforms tested – Major OSes like Windows, Linux, iOS
• Resolution coverage – Screen sizes and resolutions tested

Robust compatibility coverage ensures quality user experiences across your whole target audience.

This is not an exhaustive list, but rather the most popular types of test coverage employed today. The key is choosing metrics aligned to your testing objectives and priorities.

Now let’s examine how to actually measure coverage…

How to Measure Test Coverage

Measuring coverage starts by identifying what test items or attributes to track. Based on your test strategy, select a set of coverage goals and metrics to monitor.

Next, you need a way to quantify the coverage. Here are some common measurement approaches:

Manual Tracking

For purely manual testing without automation, coverage can be tracked manually. For example, a spreadsheet could list required test scenarios with columns to mark those executed. Or paper test matrices can track requirements versus test cases.

This manual approach requires discipline to record results consistently. But it provides basic coverage visibility even without automation.

Code Coverage Tools

Specialized code coverage tools like Istanbul, JaCoCo and Cobertura instrument code to record execution at runtime. They generate detailed statistics like line, branch, and method coverage.

Most IDEs also offer build-in code coverage capabilities. These tools are essential for unit test coverage, but address only a subset of overall coverage needs.

Automated Testing Frameworks

Test automation frameworks like Selenium and Appium can record details about test executions. For example, they can produce lists of user journeys and features exercised by test suites.

By generating coverage reports, they quantify automated test coverage without needing separate coverage tools. However, their reports focus exclusively on automated checks. Supplement with manual tracking for complete coverage visibility.

CI/CD Pipeline Reporting

Modern CI/CD pipelines integrate seamlessly with testing activities. Code coverage results can be extracted from unit test runs. Automated UI and API testing results can populate coverage dashboards.

Pipelines provide both raw coverage data and high-level reporting. They enable near real-time coverage monitoring as part of the development lifecycle.

Test Management Tools

Test management tools like qTest, TestRail and Zephyr help track test design and execution. Requirements and test cases can be linked to illustrate coverage. Manual testing also becomes more measurable by recording executions.

Such tools are purpose-built for coverage reporting. However, they involve overhead of re-entering tests executed elsewhere. They work best when integrated with automation frameworks.

Calculating Coverage

Once coverage data is collected, calculating percentages is straightforward. For example:

• Unit test line coverage = (Lines executed by tests / Total lines) * 100
• Workflow coverage = (Workflows with automated tests / Total workflows) * 100
• Requirement coverage = (Requirements with at least one test / Total requirements) * 100

Trend graphs also clearly convey how coverage evolves over time. Reports can break down coverage by system modules, test types, and other dimensions too.

Aim for 75-90% coverage for most metrics. 100% coverage is often impractical. The key is intelligently prioritizing what and how much to test.

Tips for Maximizing Test Coverage

Below are proven techniques to optimize test coverage:

Start Early

Begin evaluating coverage early, as soon as the first tests are written. Don’t wait until later to understand gaps. Agile team culture and CI/CD pipelines enable this shift-left approach.

Early feedback allows baking coverage into the development process. You can steer new code toward better coverage out of the gate.

Assess Risks First

Prioritize coverage based on risk levels. Analyze complexity, business impact, likelihoods, and other factors to identify high-risk areas. Allocate more test resources to mitigate these dangers first.

For example, test authentication and payments thoroughly before peripheral features. Such risk-driven coverage prevents nasty surprises.

Automate Broadly

Test automation is the most powerful lever for increasing coverage. Prioritize automating repetitive and multi-variant tests that are impractical manually.

Broad test automation acts as a coverage force multiplier. You expand coverage exponentially without proportional effort.

Optimize Test Data

Many times poor coverage arises from insufficient test data. Take time to design a diverse dataset covering edge cases.

Invest in tools to generate and manage test data at scale. Broad test data expands coverage to reveal more defects.

Refine Metrics

The metrics you choose drive observed coverage. Reevaluate metrics regularly to ensure they align with current quality risks and priorities.

Add metrics like API coverage for new microservices. Adjust compatibility coverage based on real user analytics. Refined metrics keep coverage focused.

Integrate Coverage Tools

Unit test, UI test, and manual testing all contribute indispensible coverage. But their results are often siloed.

Integrating code coverage tools, test management systems, and CI/CD dashboards creates a unified view. This enables making informed tradeoffs between test types.

Foster Collaboration

Ensure test coverage information flows freely between testers, developers and product owners. Make coverage everyone’s shared responsibility, not just testers’ concern.

Collaboration fills coverage gaps faster. For example, devs can augment unit tests for weak spots surfaced by system tests.

Balance Automation & Manual

Don’t fall into the trap of 100% automation. Certain tests like exploratory and usability are still most effective manually.

Build a balanced testing pyramid, with manual testing handling the unique cases machines can’t. Humans and computers working together maximize coverage.

Optimizing coverage is an iterative process. Regularly review reports to find opportunities. Increase coverage incrementally each sprint to achieve testing excellence.

How to Expand Test Coverage Quickly

Improving coverage steadily over multiple releases is ideal. But sometimes you need to expand coverage significantly within one development cycle. Here are techniques to rapidly grow coverage when time is limited:

• Crowdsource Scenarios – Conduct brainstorming sessions with QA, devs, and users to quickly build comprehensive lists of use cases, edge cases, and risks. Distill these down to test charters.
• Exploratory Blitz Testing – Organize “coverage hackathons” where the whole team dogfoods the system together for a day. Find gaps collaboratively.
• Focus Test Data – Invest in tools to auto-generate targeted parameter combinations that expose unseen defects and exercising diverse code paths.
• Buy Coverage – Purchase reusable test assets like scripts for common third-party libraries. Outsource grunt work like cross-browser testing.
• Prioritize Integration Points – Verify critical interfaces end-to-end first. Isolate them via test doubles then expand.
• Optimize Existing Tests – Improve flaky tests and prune redundant ones so each test adds unique coverage. Enrich with data variations.
• Add Logging – Instrument key areas to log runtime coverage data. Quickly reveal untested blocks without test code.

Expanding coverage significantly in short order requires creativity, discipline, and teamwork. But applying techniques like those above can achieve impressive coverage wins when you need them urgently.

Leveraging Automation to Boost Coverage

Test automation is the most effective way to drive major gains in coverage. Here are key ways to leverage automation:

• Automate Repeat Tests – Replace manual repetitive tests like happy path use cases with automated UI tests. Dramatically increases coverage at little maintenance cost.
• Expand Integration Testing – Automate API tests between services and external interfaces. Uncovers gaps not reachable via unit tests.
• Parameterize Tests – Use CSV, JSON or database data to pass diverse inputs to tests. Broadens coverage exponentially.
• Generate Negative Tests – Automatically build invalid use cases like bad input. Checks assumptions and corners.
• Reuse Steps – Centralize common test actions into reusable libraries. Minimizes new scripting for expanded scope.
• Integrate Security Scanning – Automate dynamic and static analysis into build pipelines. Automatically covers risks.
• Parallelize Tests – Use CI/CD grids and containers to execute more tests in parallel. Catches regressions faster.
• Shift Left – Move test execution earlier into dev lifecycle. Adds coverage at low incremental cost.
• Shift Left – Move test execution earlier into dev lifecycle. Adds coverage at low incremental cost.
• Analyze Results – Use AI to recommend new test scenarios based on gaps found. Maximizes learning from results.

Strategic test automation delivers outsized coverage with minimal effort. Pick the techniques that best fit your team’s skills and constraints.

Conclusion

Measuring test coverage provides incredibly valuable insights into the risks and gaps in your testing activities. Code coverage is just the tip of the iceberg of what coverage metrics offer.

Picking the right coverage goals and techniques for your organization takes some up-front analysis. But the effort pays off many times over with higher quality, lower escapes, and data-driven test optimization.

So what are you waiting for? It’s time to shine a bright light on your test coverage and never release in the dark again!

Measuring test coverage provides incredibly valuable insights into the risks and gaps in your testing activities. Code coverage is just the tip of the iceberg of what coverage metrics offer.

Picking the right coverage goals and techniques for your organization takes some up-front analysis. But the effort pays off many times over with higher quality, lower escapes, and data-driven test optimization.

So what are you waiting for? It’s time to shine a bright light on your test coverage and never release in the dark again!

TAV Tech Solutions is a leading software development company specializing in offering a complete range of software service and technology solutions across industry verticals. Test coverage is an essential part of maintaining a software during its lifecycle. It not only improves overall quality of software but also allows the testers to enhance the quality of test cases at the same time. TAV Tech experts have several years of experience in testing software to ensure businesses can leverage their tools to full potential.