QA leaders face constant pressure to meet project deadlines for software releases without compromising on quality. The best way to achieve this is by implementing test strategies focused on accurately measuring whether the tests used cover the right areas. Code coverage and test coverage metrics offer insight into the effectiveness of your testing plans, especially in DevOps and Agile environments.
What is automation test coverage in software testing?
Automation test coverage measures how much of an application’s source code executes during automated test runs. Testers use it to validate whether tests being used cover requirements, user stories, and risks important to business owners and other stakeholders.
Platforms like Ranorex execute automated scripts against the application and generate reports showing which lines of code, branches, or functions were exercised. This gives QA teams clear insight into how thoroughly the current test suite covers the codebase. In Agile and DevOps environments, where speed and repeatability are essential, automation test coverage provides a distinct advantage.
1. Faster delivery
Continuous integration and delivery (CI/CD) pipelines automatically trigger builds and tests after each code commit. When paired with automation test coverage, teams can detect regressions in minutes rather than the days manual testing often requires. This acceleration allows developers to merge changes and release updates much faster.
You can also avoid bottlenecks that crop up when performing manual regression runs. Without automated test coverage, QA testers can get bogged down and end up with delayed deployments, which slows business responsiveness. Automated coverage ensures your test pipelines keep up with rapid release schedules.
2. Improved software quality
Automated software testing eliminates the risk of a human tester becoming fatigued after executing multiple tests and missing something important. Reducing human errors raises overall quality and ensures that important test scenarios are validated without missing steps or taking shortcuts.
Teams benefit by gaining the ability to scale up test coverage as needed. You can run hundreds or thousands of tests in parallel without bringing on multiple resources to handle the load. This improves coverage by allowing for the validation of not just happy paths but also edge cases, boundary values, and cross-environmental compatibility.
For example, a company developing a healthcare app can run test suites across different platforms. This gives the business the confidence that the application functions the same on both iOS and Android devices, so that all users have the same experience regardless of the platform.
The ability to integrate automated software testing into CI/CD pipelines helps teams locate and resolve defects more quickly. This can reduce the expenses that come with discovering an issue late in production. Teams also receive instant feedback with every commit or when validating new features.
3. Boost in software release confidence
Automated frameworks like Ranorex boost your team’s ability to prioritize high-risk areas for coverage, ensuring those modules receive validation during every build. As a QA leader, you can illustrate the quality of the application to stakeholders by linking requirements coverage and risk coverage to automation. Automated testing also leads to faster releases, allowing executives to feel comfortable promoting a reliable product.
Automation test coverage vs. Manual test coverage
Automation test coverage provided by platforms like Ranorex lets QA professionals write scripts and run tests across various environments. You can write a test case once, then run it multiple times and produce consistent results.
Teams can scale their test suites to cover multiple types of tests, including:
- Functional
- Regression
- End-to-end
- Integration
In contrast, manual test coverage measures validation by human testers without help from automation. Companies still rely on manual testing when performing exploratory testing and usability tests. There are some instances where human judgment is critical, like when evaluating a user’s experience or discovering potential edge cases.
Automated testing is helpful in cases where a team needs to run a large volume of regression tests across different browsers or configurations. Manual testing is helpful when you run into a new issue that needs more research to determine the next steps to take. Below is an overview of the pros and cons of automated vs. manual testing:
| Scenarios | Automated Testing | Manual Testing |
| Coverage | Pro: Helpful when you need to perform broad regression and end-to-end testing on APIs and across different platforms, including web browsers and devices Con: Use may be limited in subjective cases, like accurately evaluating a user’s experience Best Suited For: Running large regression testing suites or evaluating end-to-end testing for critical workflows | Pro: Effective when you need to look at more subjective cases, like the usability of a platform and validating specific features Con: Takes longer to perform manual testing, which can lead to skipped tests because of time constraints Best Suited For: The early stages of outlining features, when requirements might change rapidly because of feedback from other team members and stakeholders |
| Speed | Pro: Allows QA teams to execute thousands of tests in a matter of minutes. This helps get through multiple regression testing scenarios consistently in different environments Con: May not be able to handle more complicated scenarios that require more objectivity and critical thinking Best Suited For: CI/CD pipelines that require quick feedback | Pro: Allows for more thorough review of a specific feature. Testers can adapt to evolving scenarios Con: Takes longer to run tests. Teams are limited by the number of available resources and time, which lowers the ability to scale as new features are added Best Suited For: Any regression testing where the automation ROI is low and for ad-hoc testing |
| Scalability | Pro: Automation frameworks can run multiple tests across different OS versions, environments, and devices Con: The costs associated with the tooling of some automation platforms may be high for smaller organizations Best Suited For: Teams with a need to scale tests in DevOps environments that require frequent deployments | Pro: Preferred when you want to avoid complicated tooling. Testers can quickly start running test scenarios Con: Often not feasible when there’s a need to work through large regression suites or execute test cases on multiple environments Best Suited For: Teams working on small projects, short-term validations, or proof-of-concept releases |
| Accuracy | Pros: Helps teams achieve reproducible results without worrying about fatigue that causes human errors Cons: Can miss issues in situations that require more discerning judgment Best Suited For: Teams running performance checks, regression testing, or working on API validation | Pros: Testers are more likely than automation to catch more subtle issues like UI glitches and accessibility problems that affect the user experience Cons: There may be inconsistencies in running tests or steps missed that affect the quality of a test Best Suited For: Exploratory and usability testing, along with accessibility validation |
Automation Test Coverage vs. Code Coverage
While automation testing looks at how much of your code gets evaluated through test automation, code coverage measures the amount of an application’s code reviewed by any testing, whether manual or automated. It looks at the extent to which testing reviews lines of code, functions, branches, and various pathways.
Below are the pros and cons of working with automated test coverage and code coverage when evaluating code scope and function:
| Scenarios | Automated Test Coverage | Code Coverage |
| Scope | Pros: Teams get insight into how much of the code gets reviewed by automated tests, including regression, UI, and API code. Teams can cover more ground in minutes versus waiting days for test suite completion Cons: Automation can lead to predictable workflows that may struggle with unexpected test scenarios. May not provide adequate coverage of subjective areas Best Suited For: Locating unused or obsolete code areas and supporting regression safety, which reduces the risk of code leaks | Pros: Provides users with a clear benchmark of how much code gets evaluated during testing. Helps testers identify gaps like untested code paths. Also helpful in industries like finance and healthcare, where auditors may want proof of systematic codebase coverage Cons: Can lead to complacency among testers in not catching poorly written tests that may inflate numbers without providing meaningful validation. Attempts to achieve high code coverage percentages can require a lot of effort, and have diminishing returns above a certain percentage Best Suited For: Situations where teams need to provide measurable metrics for audit showing they are reviewing critical application areas |
| Function | Pros: QA testers can map automated test coverage directly to functional tests, like reviewing login paths, checkout, and API requests and responses. This ensures that critical workflows receive consistent validation Cons: Poorly designed tests can lead to situations where an application’s behavior doesn’t get reviewed in all contexts, like edge cases or risk coverage Best Suited For: Running tests to evaluate application functions within all builds, environments, sprints, and releases. This improves the reliability of business-critical test cases | Pros: Provides clear feedback on whether a codebase’s functions were executed during testing. It helps locate whether a function has been missed during previous test runs, which can be an indicator of possible defect leakage Cons: Code coverage does not provide details on whether a test produced a correct result or if it met functional requirements. May focus on obscure functions while overlooking critical user-facing items Best Suited For: Making sure all functions in a codebase undergo testing |
Teams can benefit from using both automation test coverage and code coverage in some scenarios.
- DevOps/Agile regression testing: Automating regression test suites can help teams handle rapidly growing test suites, while code coverage can detect any new paths introduced via code updates.
- Evaluating new features: Using automated tests allows you to validate requirements consistently, while code coverage highlights areas where you may be missing test cases.
- Reviewing high-risk business area workflows: You can ensure validation of critical user functions like login with automation, while code coverage can confirm the execution of tests for backend validation, alternate branches, and error handling.
Best practices to maximize automation test coverage
Beyond automation, QA leaders need to invest in strategies that maximize the benefits of using automation test coverage. Below are some best practices to help guide teams:
1. Risk-based prioritization
Some parts of an application are more important than others. You don’t want to waste resources and potentially delay releases by focusing on non-essential areas. Instead, focus your automation on places with the highest business and risk impacts. High-risk areas include your core user workflows, like checking out and payment processing.
2. Use of historical defect data
Review your defect trends to gain insights into where automation provides you with the biggest gains. Teams can avoid guesswork and find weak spots that affect an application’s reliability. For example, you can look for recurring defects generated consistently from certain modules. These are places where you should prioritize automation and track certain defect types for automatic test case generation.
3. Automated script maintenance
An outdated or broken script can cause false positives or leave coverage gaps, leading to a loss of confidence. Script updates should occur as part of a team’s regular sprint deliverables. Use test management tools to link scripts to requirements coverage to ensure testing remains aligned with business goals. Finally, teams should retire obsolete tests and update existing tests so they reflect new features and functionality changes.
4. Parallel automated tests
Running tests in parallel allows you to cover more of an application’s code base in less time. This helps teams avoid the bottlenecks that can occur when trying to run large regression suites in sequence. Tools like Azure DevOps or GitHub actions help orchestrate parallel execution.
5. Robust Tooling
Solutions like Ranorex Studio give teams what they need to fully implement their coverage strategy. It provides cross-platform support and reporting, and integrates directly with CI/CD pipelines, allowing QA to cover various test types and align with modern DevOps practices.
Types of test coverage and what they measure
Below is an overview of different test coverage types:
1. Functional coverage
This metric confirms that all features reviewed behaved as expected and that system functionality matches the business requirements. For example, a company building a portal used to book patient appointments would validate that:
- Patients can search for different time slots
- Patients receive booking confirmations via email or text
- Appointments get registered within the doctor’s schedule
2. Requirements/user story coverage
This measure informs QA testers whether every requirement has an associated test case and provides traceability between business goals and testing. Testers would use this metric to confirm the following from the requirements:
- That patients can review lab results within 24 hours of release
- That patients receive a notification that test results are ready
- That controls are in place to protect a patient’s health information from being viewed by others
3. Code path and branch coverage
Teams use this measure to confirm that all decision points in a code base get executed during testing. When evaluating a claims processing application, this metric would check for the execution of code paths and branches like:
- Approving a valid claim
- Denying an invalid claim
- Checking for prior insurance authorization
4. Cross-platform testing
Teams apply this measure to verify how an application performs across platforms, browsers, and devices, ensuring a consistent user experience. For example, a telehealth platform offering video consultations would validate that the feature works on Edge, Chrome, and Safari; that the app remains stable on iOS and Android devices of varying screen sizes; and that camera and microphone permissions function reliably.
Key metrics to measure automation test coverage
Automation test coverage goes beyond executing a specific number of tests. The goal is for these tests to provide you with information on how well tests perform in validating requirements, lowering risk, and stopping defect leakage. Let’s look at some of the most important metrics used in QA evaluation:
1. Percentage of test cases automated vs. total
This metric tells you the proportion of automated test cases run versus total test cases, meaning you’re adding together automated vs. manual test cases. A team working on a banking application might automate 80% of regression test cases, evaluating transactions and fund transfers and run the remaining 20% manually.
2. Automation pass/fail rate
This metric shows the rate of automated test cases that passed versus those that failed. A high failure rate can indicate the use of unstable scripts or unreliable test data. If a team sees a failure rate of 15% when running API tests on an electronic health record (EHR) solution, they can work to pinpoint problem scripts and make automation more stable.
3. Defect detection rate
This measure provides a ratio of defects found by automation versus defects found during testing. It shows the automation’s effectiveness in catching bugs. If automation manages to catch 90% of defects when evaluating API responses in an app before release, that can vastly improve a customer’s experience.
4. Cross-platform coverage
This metric looks at how many environments are included in automation runs. It’s a way to ensure that users get the same experience regardless of their device or OS, leading to improved customer satisfaction. A technology company working on a software-as-a-service platform would use this metric to confirm that tests get run on Windows, Linux, and macOS to confirm desktop consistency.
5. Requirements and user story coverage
This metric is a percentage of all requirements and user stores validated by automated test cases. It links to business requirements to avoid missing critical features during testing. Teams would use it to ensure all secure transactions in a finance application receive 100% coverage.
Strategies to improve automation test coverage
Let’s look at how teams can implement strategies that improve the effectiveness of their test coverage. Imagine that we have a QA team running tests on a new financial trading application for users interested in retail investments.
Use automation metrics to identify gaps
Look at metrics like your percentage of test cases automated and defect detection rates to find where there may be missing or ineffective tests. This gives you a clear view of where you might want to expand coverage.
Make high-risk areas a priority
Automate your high-risk areas first. In your trading app, this means covering scenarios where trades might be frequent and evaluating conditions like whether orders are valid or if there are insufficient funds available.
Integrate automation into CI/CD pipelines
Trigger automated tests upon each new code commit to ensure that the codebase undergoes regression and functional tests.
Monitor and iterate
Keep track of where defects appear in the trading application. You may see low defect rates in automated suites but find more with manual testing. This indicates that you may need to expand automation into new edge cases.
Common automation test coverage mistakes and misconceptions
Here’s what you want to avoid with automation test coverage.
Believing 100% coverage makes the app bug-free
While the coverage metric is very useful, it only tells you how much of the codebase your tests review. It doesn’t give any insight into whether the tests are meaningful. Some teams make the mistake of pushing for 100% and boosting the score with low-quality or redundant tests.
Over-automating low-value tests
Focusing your tests on rarely used features or cosmetic UI elements should be left to exploratory testing. Teams can end up with bloated test suites that take forever to execute.
Allowing automation coverage to decay
Don’t make the mistake of setting up automated test coverage and then not maintaining the scripts. That leaves them prone to breaking when new features get introduced.
Achieve optimal test coverage with Ranorex Studio
The best way to achieve organizational testing goals is by using tools that accurately reflect the effectiveness of your test suites. Ranorex Studio provides teams with an all-in-one test platform designed to manage the needs of automated testing for desktop, web, and mobile applications. Keep pace with Agile and DevOps demands. Start your free trial of Ranorex Studio to automate coverage, catch regressions faster, and deliver reliable software at scale.
