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Code coverage and test coverage are two terms that are commonly used in discussions about improving code quality and enhancing software reliability. Although these terms seem similar, they serve different purposes. Think of it like you are preparing for a big exam, and you have two study plans in mind to ensure you ace the exam. Code coverage in this scenario focuses on ensuring that you have covered every chapter of your study materials, while test coverage goes a step further to ensure you have studied for every possible question that could be asked in the exam.
This article will examine the differences between these terms and help you understand how each contributes to building better software.
As mentioned earlier, code coverage and test coverage are two important metrics in software development, however, they measure different aspects of testing.
Code coverage tells you how much of your codebase has been tested i.e. which parts of your codebase have been tested and which have not. Test coverage, on the other hand, measures the extent to which the application’s requirements, features, and scenarios have been tested. One thing to keep in mind is that high code coverage does not guarantee that all the possible scenarios have been tested, while, a high test coverage means that the tests cover a wide range of scenarios, even if not every single line of code is executed.
Let us look at some key differences between them:
Aspect |
Code Coverage |
Test Coverage |
Focus |
Measures the percentage of source code that has been tested. |
Measures how many possible test scenarios were covered by the tests. |
Qualitative vs Quantitative |
Primarily quantitative and focuses on the number of codes tested. |
Primarily qualitative and focuses on the completeness and quality of the testing process. |
Testing Approach |
Typically associated with white-box testing, where the internal structure, design, and implementation of the software are used to design test cases. |
Associated with black-box testing, where the tester does not focus on the internal code structure and test cases are designed based on the software’s functionality, requirements, and outcome. |
Metric |
Expressed as a percentage of code elements (lines, branches, functions) that are covered by tests. |
Expressed as a percentage of use cases, scenarios, or application paths that are covered by tests. |
Testing Level |
Generally measured during unit testing by developers. |
Measured during higher levels of testing such as integration, system, and acceptance testing. |
Types of Coverage |
Includes branch coverage, line coverage, and function coverage. |
Includes product coverage, requirements coverage, risk coverage, features coverage, etc. |
Ideal Use Case |
Ideal for identifying dead and redundant code. |
Ideal for validating that the application meets the requirements and behaves as expected across various scenarios. |
Complexity |
Easier to measure and quantify as it involves counting the executed code elements. |
Not easy to measure as it involves evaluating the thoroughness of tests across different scenarios. |
Having looked at the differences between code and test coverage, let us look at these terms in more detail.
When it comes to testing how the software performs, test cases are like the checklist you use to measure that software does what it is expected to do. Test coverage builds on this concept by measuring how effectively these test cases cover the application’s requirements, functionalities, and scenarios. Test coverage helps to answer the question: “How thoroughly are we testing or have we tested the application’s features and functionalities?”.
Test Coverage is important for the following reasons:
Test coverage is an important aspect of quality assurance, and it can be categorized into different types based on the aspect of the application being tested. Here are the different types of test coverage:
For example, if you are testing an e-commerce website, and the requirements include user registration, product search, adding items to the cart, and checkout, requirement coverage will check if each of these functionalities has corresponding test cases.
Let’s say you are testing a mobile app that has about 20 features and your test cases only cover 17 out of the 20 features of the application. Using the formula below, the feature coverage would be (17/20) x 100 which would give 85%.
If after conducting a risk analysis on a software application, 20 high-risk areas were identified and the test specifically targets and addresses all these areas, it means the risk coverage is 100%, however, if the test did not address all the risk areas, the test coverage value decreases.
For example, if a software product has 12 major components and 10 are tested, the product coverage is (10/12)×100=83.33%.
Suppose an application needs to be tested on 10 different browsers and operating system combinations, and tests have been conducted on 8 of these combinations. The compatibility coverage would be (8/10)×100=80%. This indicates that 80% of the required environments have been tested for compatibility.
To ensure that a software application is thoroughly tested and reliable, there is a need to improve test coverage. Here are some techniques to improve test coverage:
Test coverage in manual testing, also known as manual test coverage, is the extent to which an application’s requirements, functionalities, and features are covered by manual testing activities. Manual test coverage is important because it helps to ensure that all aspects of the application are tested, reducing the likelihood of undetected defects. It also improves quality, helps to identify areas that may have not been thoroughly tested, and mitigates risks by addressing them before the application is released.
In as much as manual test coverage is good, it can be a lot better with automation. Here are some ways in which automation impacts test coverage:
A good test coverage percentage typically ranges between 70% to 90%, with 70-80% being a balanced target and 80-90% ideal for critical applications. Coverage in this range ensures that the application’s most important components are thoroughly tested, identifying possible problems early. While aiming for greater coverage might be advantageous, particularly in high-risk applications, focusing too much on reaching 100% could have decreasing effects.
Code coverage is a metric used to measure the percentage of code that has been executed by tests. This helps to provide information about the effectiveness of the tests by indicating which sections of the codebase have been tested and which have not. For example, if you have 100 lines of code and your tests execute 80 of those lines, your code coverage would be 80%.
Code coverage answers the question: “How much of the code has been executed by the tests?”
Code coverage can be categorized into various types, each focusing on different aspects of the codebase. Here’s a breakdown of the most common types and how they are measured:
Using the above formula, if a codebase has 150 lines of code, and the tests execute 80 of those lines, the line coverage would be calculated as (80/150) x 100 = 53.3%
Function coverage measures the percentage of functions that have been executed by the test cases. This helps to ensure that each component of your code is functioning correctly and integrates well with the rest of the system.
For example, if a codebase contains 15 functions, and the test cases execute 14 of these functions, the function coverage would be (14/15) x 100 = 93.3%
Branch coverage ensures that each possible branch in the control flow of a program is tested at least once. This helps identify untested paths and ensures that all logical conditions have been evaluated.
Here are some effective techniques to enhance code coverage:
Good code coverage depends on the context of the project and its requirements. Many teams aim for about 80% code coverage, though this is not a strict rule. Aiming for code coverage of 90% or above might be preferred for critical systems or those with high risks, while 60-70% might be acceptable for less critical or exploratory projects, provided it still ensures quality and reliability.
In summary, when trying to ensure software quality, both code and test coverage play important yet specific roles. Choosing between either of them depends on your project’s goals and stage of development. Test coverage is more useful for validating functionality and requirements, especially in early development or a test-driven approach, while code coverage ensures that all code paths are exercised, making it more relevant as the project matures. Incorporating both metrics would be an effective strategy and balanced approach for achieving comprehensive quality assurance.
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