Code coverage vs Functional Coverage

The concept of coverage, also commonly used in software systems testing, denotes how well the tests sufficiently challenge the tested system. Code coverage and functional coverage are, thus, the two main categories of coverage. Although they cover distinct topics, both approaches provide information regarding the degree of test coverage and sufficiency. The same information is provided in this article about functional coverage, such as what code coverage is and when it should be checked.

What does Code Coverage mean?

How to Determine Code Coverage

To ascertain whether or not your code was exercised throughout the run of your test suite, code coverage tools will employ one or more criteria.

Typical metrics that your coverage reports may have stated are as follows:

• Function coverage: the count of calls made to all defined functions.
• Statement coverage: the number of times the program’s statements have been run.
• Branch coverage: the number of executed branches (if statements, for example) within the control structures.
• Coverage of conditions: the number of boolean sub-expressions examined for true and false values.
• Line coverage: the quantity of tested lines in the source code.

These metrics often take the form of the number of items that were tested, the number of items discovered within your code, and a coverage percentage (items tested / items found).

These measurements are different yet connected. The simple script that follows contains a Javascript method that determines if an argument is a multiple of 10. Later, we will utilize that function to determine whether or not 100 is a multiple of 10. It will make the distinction between branch coverage and function coverage more accessible to understand.

Achieving complete testing coverage is a goal shared by numerous teams. Is code coverage for tests sufficient as the metric? Sadly, no. Functional coverage is not the same as code line coverage. In the end, what matters most is complete functional coverage.

Examine a basic approach for formatting a string.

public static string Format(int? value)

{const int defaultValue = 42;

if(!value.HasValue)

{value = defaultValue;}

return “The value is ” + defaultValue + “.”;}

For this technique, there exists an accurate test that yields 100% code coverage. There’s a severe bug in there yet. Can you identify it? (If you don’t, simply read on; it will become apparent when a later test is added.)

The Test

[TestMethod]

public void Test()

{var result = ValueFormatter.Format(null);

Assert.AreEqual(“The value is 42.”, result);}

As a result of entering the if statement in the tested method and executing every line of code, this test provides full code coverage. However, it falls short of giving full functional coverage since two distinct scenarios involving the use of a default value and a specified value must be tested separately.

The Correct Tests

Even with this simple strategy, one test case is insufficient. There must be two distinct cases.

[TestMethod]

public void TestDefaultValue()

{var result = ValueFormatter.Format(null);

Assert.AreEqual(“The value is 42.”, result);}

[TestMethod]

public void TestGivenValue()

{var result = ValueFormatter.Format(7);

Assert.AreEqual(“The value is 7.”, result);}

The old test, now called TestDefaultValue for clarity, is identical. The absent test is the second one. The first one tests whether the method functions correctly without an if statement. The bug is visible. Incorrect usage of the value variable in the string formatting should have been the defaultValue constant.

The percentage of a program’s functioning source code that is executed line by line when a test suite is being performed is known as code coverage. The percentage of code that can be tested in relation to the actual code is measured to determine the level of test coverage.

Various tools are used to gather code coverage; debuggers, profilers, and instrumentation may be used in the compilation process. Opening the box to reveal which portion of the implementation is covered by tests offers a means of determining test coverage. A large percentage of the system’s code has been tested according to high code coverage numbers.

The Advantages of Code Coverage

Measuring code coverage has the following advantages:

– Identifies code clones, or code that has almost identical functionality but comparable blocks of code that may have a different name. These codes can be eliminated.

– Provides a list of all the methods and classes that are not tested, which is helpful in highlighting untested code.

-Encourages improved test procedures: developers want to increase coverage, which entails enhancing the tests as well.

-Code coverage standards, such as 80% branch coverage, are typically provided by teams prior to a release and are a good indicator of testing effectiveness.

Restrictions on Code Coverage

Code coverage is helpful, but it has certain drawbacks.

– Only the test process, not the functioning itself.

– The phrase “coverage 100%” does not always imply functionality that has undergone thorough testing.

-It’s evident that code could have errors even if it is covered; additionally, just because code runs doesn’t guarantee that it will function as intended.

– A function called 100 times will appear to have a very high coverage statistic; however, coverage stats can be somewhat misleading.

– User interactions can be meticulously modeled despite the GUI code’s low coverage.

Additional metrics that indicate the scope of testing would be necessary to determine the total level of code coverage. However, reaching a high coverage level does not guarantee accurate or comprehensive testing.

Functional  Coverage

Covering design functionality or feature metrics is the focus of functional coverage. This user-defined measure indicates the extent to which a design specification or feature has been used. Two categories for the functional coverage exist.

1. Data intended coverage: To determine whether particular data value combinations occur.

One example would be to write several data patterns in a register.

2. Control desired coverage: This involves making sure that sequences occur in the intended order.

As an illustration, read a register to obtain reset values following the release of a system reset.

Because these metrics are user-defined, the verification engineer must take into account every feature. If, however, some features are neglected to include in the functional coverage, and the remaining features are covered, the functional coverage will still be 100%, even though some cover points are missing.

The tests missed an issue that was caused by a straightforward use of the wrong identifier. Even though the line coverage shown in the code coverage report was 100%. Our lack of functional coverage was the issue. Why is code coverage used as a statistic while functional coverage is what counts? The reason is that it is tough to quantify functional coverage. Both measuring results automatically and manually checking them are challenging. Many redundant tests that are expensive to design and maintain will result from attempting to achieve complete functional coverage for an already-existing, more extensive code base.

Functional coverage addresses the externally observable behaviors and functions of the system from a black box perspective, whereas code coverage adopts an implementation view. Functional coverage is the extent to which a test suite has examined the identified functions, use cases, and requirements.

Here are a few instances of functional coverage metrics:

This is a test to see how well all conditions have been met verified, and can be stated as follows:

• Is the use case covered completely? Have all of the processes and flows been carried out and tested?
• User stories for test coverage
• How many user stories are subjected to testing?
• Coverage of features: Have all features been located and looked at?

Functional coverage, as opposed to internal test coverage, evaluates the effectiveness of all external interface tests and, as such, provides the most accurate picture of how thorough testing has been from the standpoint of users.

The Advantages of Functional Coverage

A few of the main advantages of functional coverage are as follows: – It measures if promised functionality was supplied; – It helps with test planning; – It enables the identification of areas not tested; – It correlates customer satisfaction.

– Can be quantized per specifications before coding, even when the details supplied don’t need to be coded.

– Beneficial for reporting – easy to use and quick for participants to finish

Facilitates risk-based testing by assisting in identifying the most critical and dangerous components of the application.

Adding functional coverage to code coverage aids in thorough testing completeness monitoring.

Code coverage can tell you how much of your source has been tested. Here, we’ll look at how you may begin working on your projects using this beneficial measure to evaluate the caliber of your test suite.

If writing covering tests for the code is not practical, then what should be done?

Conversely, put it this way: As soon as any new functionality code is written, ensure a covering test is included. First, construct the test to ensure the desired feature is functional, then write code to ensure the test passes. Is that something you’ve heard before? Given that this is Test Driven Development, it should (TDD).

Test-Driven Design

TDD is the only approach that  ensures every functionality is tested without producing excessive duplicated tests. There won’t be any functionality in the code without a test if the tests are allowed to lead the development of the functionality. Because a test is only worthwhile to build if it drives any new functionality, it will also reduce test redundancy. Tests for current functionality are not written in TDD.

They must be covered to fully benefit from automated testing; otherwise, you will have to perform laborious manual testing as well. Because you are afraid of breaking something that is not tested, you won’t refactor. If you are not very strong, you will probably stop believing in automated tests and let them go bad, not caring about fixing the tests that don’t pass.

However, this is the only approach that provides complete functional coverage and ensures that tests will be produced because of how closely they are incorporated into the creative process.

Conclusion

In summary, one may say that functional coverage and code coverage are two different measures that provide similar, albeit conflicting, information about the testing condition. Targeting the implementation code and code coverage provides an internal perspective of the program and its testing. Functional coverage, on the other hand, focuses on external behavior as a specification or function to determine whether it has been used.

In order to cover all the required regions, a variety of coverage types with the aid of specific metrics and technologies are advised. Both strategies have advantages. Code coverage guarantees that the quality of the implementation is as expected while tracking functional coverage contributes to increasing confidence that the components of the system that users can see are in order. Taken collectively, they result in a comprehensive confirmation of the system’s accuracy and functional identity.