Code Coverage vs Test Coverage: Differences You Need to Know

People often use the terms – code coverage and test coverage interchangeably, which is wrong because they are two different things. As both of them are used to measure the efficacy of code, the terminologies get tricky at times for the development and testing teams. So if you are wondering how – code coverage is the evaluation of the code that is executed, and test coverage is a measure of the features being tested that are covered by the test. In this blog, we will dive deep into each aspect of code coverage vs test coverage and discuss the code coverage vs test coverage differences in detail. 

While code coverage is a quantitative measure (number of lines of code executed by tests) of the effectiveness of code, test coverage is a qualitative approach. code coverage in testing aims to assess if the test cases are covering entire functional requirements. 

The core objective of both code coverage and test coverage remains the measurement metrics leveraged to simplify the assessment of the quality of application code. These are significant for developers to rectify any errors if discovered and enhance the overall quality of an application.

Why is Coverage Important?

Each piece of code that is sent to production needs to be thoroughly tested in terms of quality and effectiveness. Developers need to ensure every possible real-life scenario is handled well by the application. Hence, coverage – which refers to the evaluation of the efficiency is vital for your product and its smooth functionality.

Here are a few reasons why coverage is important for your application:

1. Identify and remove errors at early stages: Coverage helps you discover potential issues in requirements, test cases and errors during the early stage of your development cycle. Hence, coverage saves you from a lot of hassle later on. 

2. Eliminate unnecessary test cases: Identifying and eliminating those needless tests becomes possible due to coverage. This aspect is immensely helpful in making the code lighter and testing a bit faster.

3. Identify unreached areas: Coverage enables developers to discover areas of a program left out by a set of test cases. So, your program becomes vigorous and has the least number of errors.

4. Enhance your ROI: Better resource utilization led by coverage ensures much fewer issues and errors at production stages and hence the least possible errors while in the user testing stage. That further maximizes your ROI as not as many resources are required to rectify defects later on.

What is Code Coverage?

At its core, code coverage is a software testing metric defining the number of successfully validated lines of code in a test procedure. Code coverage testing helps in determining how thoroughly software has been tested. 

Both the methods – manual and automated testing are considered for test cases that include the number of lines of code. The idea is to measure the number of lines of code verified by the testing procedure and the total number of lines of code in a software component. So that the possibility of missing out on errors diminishes even when dealing with lengthy codes.

The primary aim of every development team is to deliver enterprise-grade software with the least count of bugs possible. To accomplish that, it is crucial to review the software by analyzing, monitoring and measuring the test procedures. It is where code coverage tests comes into the picture as an effective metric to evaluate the overall testing’s efficiency and completeness. Let’s take a look at some more advantages of code coverage.

Advantages of Code Coverage

You are already aware that testing code coverage enhances the effectiveness of test code and allows you to improve the coverage performance. But why exactly does the efficiency boost up? Here are the top 3 benefits of code coverage:

• Improved speed and productivity: WIth the help of code coverage, developers are able to wrap up the software development cycle faster. Subsequently, the productivity goes high and more products can be delivered within a limited timeframe. As a result, your customers are more satisfied than ever and you witness a considerable increase in ROI. 

• Hassle-free management of code base: Maintaining your code base effectively and ensuring scalable, high-quality code is vital. Code coverage helps you analyze your code quickly so that you don’t fret while adding new features. 

• Elimination of poor code: Code coverage does not only help optimize the entire code base but also helps you identify bad and unused code. A couple of things improve through this – product maintainability becomes better, and developers learn about avoiding bad and redundant code.

How to Measure Code Coverage?

Measuring code coverage is quite simple because it’s a quantitative analysis. Here’s how you can calculate the code coverage for a software component:

Code Coverage (in %) = (Number of lines of code that have been tested/Total number of      lines of code in the corresponding software component) * 100

For example – Consider testing your software component which has 1000 lines of code. Now out of the entire code component, if 800 lines of code get validated – the code coverage becomes 80%.

In that case, you might be wondering how to reach 100 per cent of code coverage – that would be better after all, isn’t it?

Well, it’s not necessarily always better. We will discuss more on this later.

What is Test Coverage?

The first major differentiator of test coverage is unlike that of code coverage, this is a black-box testing approach. It primarily measures the number of tests that have been executed, and whether the active test cases are covering the maximum part of the numerous documents including

• Functional Requirements Specifications (FRS)
• Software Requirements Specifications (SRS)
• User Requirements Specifications (URS)

After all the functionalities mentioned in the documents are executed, test code has to be developed to validate the implemented product features. The objective is to provide metrics on tests being executed on a software solution. 

Test coverage in software testing involves numerous testing techniques like unit testing, responsive testing, cross-browser testing, integration testing and acceptance testing. The evaluation and measurement of test coverage are then done based on a number of features covered by the test code.

For example: Suppose you have been assigned the task of writing test cases of a new user signing up. The test coverage should validate all the possible real-life test cases including available username, unavailable username, valid password set, invalid password initiation and others.

Advantages of Test Coverage

Test coverage is a valuable metric that caters to the much-needed measurement of functional efficiency of software components. Here are the top benefits of test coverage:

• Identification of untested code components: Test coverage helps you discover parts of the codebase that have not been directed by various relevant test cases.

• Helps developers design new test cases: To optimize the overall test coverage, you can create new test cases based on requirements that can be analyzed from the existing test coverage. 

• Detection of redundant test cases: Along with suggesting new test cases, the analysis of test coverage will also help you detect useless test cases that aren’t utilized in the current set of requirements. So that you can eliminate those test cases and improve the code.

How to Measure Test Coverage?

Measuring test coverage is not as simple as measuring code coverage because of the qualitative nature of test coverage. Numerous techniques exist to evaluate the test coverage based on the application and industry. Although a majority of test coverage measurement methods are very specific to certain relevant cases and cannot be used out of scope, some popular ones can be applied to every project:

1. Test coverage by functionality: T, his technique involves linearly verifying if all the features of your application are being validated by tests. You just need to create a list of all the functionalities of the application and start evaluating them one by one. Even though this method doesn’t throw light on how well those features are tested, it definitely does the hard check on the number of validated functionalities.

2. Test code coverage: One step ahead of the first method, this test coverage technique uses a dedicated tool to return the percentage of code that got implemented while testing, which is mostly automated. Again, in this case, the execution of code doesn’t guarantee the correct results for any of the tests. However, information about how much of the system code got executed helps in determining the test coverage and optimizing it further.

3. Test coverage by User Interface: The primary aspect which is directly related to the end-users and hence needs to be thoroughly examined is the UI of your application. So UI elements of your application like frames, navbars, pop-ups, buttons, drop-downs, scroll bars and others need to be considered by your tests and all the interactive actions lead to desired results through the user interface.

4. Test coverage by journeys: Each possible path that a user can follow to get the desired results through your application is a journey. Every URL of your platform along with buttons, internal links and menus leads to a result and hence should be comprehensively tested.

Code Coverage: Metrics, Mechanisms and Tools

With the continuous addition of features and fixing of errors, test codes also need to be updated correspondingly. So to keep the efficiency of testing intact, it’s vital to set minimum acceptable testing standards. Code coverage is the ideal metric to analyze and improve your overall code quality and testing strength.

But how do you execute code coverage? What are the best tools that would help you do it successfully? Let’s find out without wasting a moment.

Types of Code Coverage

Numerous types of code coverage exist in software testing. It is important to note that each of them aims for a different part of the code:

• Branch coverage: Assessing the code coverage when the code is a ‘branch’ statement or decision-making statement is referred to as branch coverage. For example – if-else, do-while, switch. All of these are branch statements and branch coverage ensures executing them at least once.

• Function coverage: Closely similar to branch coverage, the function coverage facilitates the execution of each function while performing code coverage. Additionally, function coverage checks if different types of arguments are passed onto the function to verify the efficient functionality.

• Condition coverage: This type of coverage tests the conditions that are present in the code. A condition coverage technique verifies the accuracy of subexpressions and variables inside all the conditional statements.
  
  For ex: if (marks>40 and attendance == ‘P’)

A branch coverage checks if any of the 4 possible conditions above are satisfied and the code successfully gets inside the block.

• Statement coverage: One of the simplest coverage – the statement coverage in testing just verifies that all the executable lines of code have been implemented at least once.

• Loop coverage: This approach checks if a loop is functioning accurately or not. The definition of loop coverage varies for people using it for different applications and looping conditions. For most people, loop coverage validates entering the loop once. While for the others, it’s more than one time. 

How to Perform Code Coverage?

Code coverage confirms through instrumentation. It ultimately tracks performance, discovers bugs in the source code and detects tracked information.

Here are the different types of instrumentation:

1. Code Instrumentation: Once the instrumentation statements have been appended, the source code is executed. It starts with a compilation which can be optimized using the normal toolchain.

2. Intermediate Code Instrumentation: Byte codes are inserted into the compiled-type files.

3. Runtime instrumentation: Instrumentation elements collect information when the code is running i.e. from the runtime environment.

Tools to Perform Code Coverage

A variety of code coverage tools are available according to the compatibility with different programming languages. 

• Coverage.Py (Python)

It is a python-oriented code coverage tool that informs the tester about the status of each component of code – whether it was executed, not executed, or could have been executed but wasn’t. Coverage.py can be installed easily using the command – pip install coverage.

• JaCoCo (Java)

JaCoCo is a java code coverage tool. It is run by JUnit tests and delivers the results in binary format.

• Testwell CTC++ (C++)

One of the most popular code coverage tools – Testwell CTC++ needs to be downloaded from third-party websites and it is compatible with C++ compilers. The differentiator of this tool is its ability to inform you how many times each line of code got executed rather than just informing whether a line was executed or not. 

Test Coverage: Metrics, Mechanisms and Tools

While code coverage helps you verify if each code in the software application is being executed by existing tests or not, test coverage indicates whether those tests are covering all the functional requirements of the application or not. 

Types of Test Coverage

When it comes to qualitative measurement of your application’s functionality – various aspects form it together. From functional requirements to system requirements and risk minimization, here are the different types of test coverage:

• Requirement coverage: The most important test coverage is requirement coverage and it aims at catering to all the user requirements. 

• Risk coverage: It is centred around identifying things that can potentially be risky for the software application and the users. Risk coverage is entirely dependent on the type of application you are developing.

• Product coverage: It comprehensively covers the product and validates the software product’s strength to withstand complex situations. The core objective is to test if the product works fine in extreme conditions too

• Boundary value coverage: Can you recall the boundary value analysis in testing? This type of test coverage verifies the system for boundary values in numerous inputs. Ideally, the boundary value coverage should be combined with other types of test coverage as the last thing you would want is your software accepting figures it strictly should not.

How to Perform Test Coverage?

Test coverage can be executed by leveraging different types of testing. However, businesses have varied requirements and hence which test should be run for test coverage depends on their specific requirements. Some of the most popular techniques to perform test coverage are

1. Unit Testing: Each component referred to as a “unit” is tested separately. This approach makes it easier to find and rectify errors. 

2. Functional Testing: All the features of an application are tested by referring to the FRS (Functional Requirements Specification) document.

3. Integration Testing: After all the modules or units of the application software are tested individually and integrated, integration testing is performed. The system is tested as a whole.

4. Acceptance Testing: Also known as user acceptance testing, this approach is used to verify whether a software application is ready to be shipped to the end-user or not. 

Tools to Perform Test Coverage 

• PyUnit 

The tool is primarily designed for unit testing and is one of the most popular test coverage solutions. A Python-based framework, PyUnit ensures smooth development of test cases, tests, suites and test fixtures. 

• JUnit

It is an open-source unit testing framework compatible with Java. JUnit is utilized by developers to deliver mundane tasks like writing, executing and analyzing tests. Interestingly, the tool is powerful enough to cater to user interface and regression testing synonyms. 

Now its time to choose one by analysing code coverage vs test coverage, get the answer below.

Code Coverage vs Test Coverage: Which One Should You Choose?

It’s now time to address the elephant in the room – code coverage vs test coverage: which one to choose and why. As both of these are completely different measurement approaches, it won’t be justified to choose between the two. Code coverage is a quantitative measure and is more related to the white-box testing technique. While on the other hand, test coverage is a qualitative measure and a black-box testing approach. 

So there’s no exact answer to this question “code coverage vs test coverage: which one to choose “, as it would entirely depend on your business requirements and the intricacy of the software application. In a majority of the cases, however, both test coverage and code coverage are used.

Summary

People have always mistaken both test coverage and code coverage to be the same term. But they don’t have anything in common!

For all the testers, having an in-depth analysis of code coverage and test coverage is vital. Not only do these techniques offer efficient testing, but also help developers save hundreds of hours and executives save resources.

Additionally, 100% code coverage should not be sought at any stage of SDLC because it will end up wasting more time and resources as developers would miss out on vital testing modules when short at the time.

None of the code coverage or test coverage is a perfect choice, but you should try both of them for a considerable amount of time. The combo would most likely yield the ideal results.