Code Coverage vs. Test Coverage
Published: 2023-09-20 11:50:38

**Code coverage** quantifies the portion of code lines activated during testing, while **test coverage** gauges the degree to which tests encompass the software's capabilities or specifications.

- Code coverage reveals which code segments were triggered or bypassed.
- Test coverage highlights the functionalities assessed from an end-user perspective.

Both metrics are vital in gauging the thoroughness of the testing procedure. However, individually, they provide only a fragmentary insight into the overall testing quality.

## What is Code Coverage?
This metric evaluates the lines of code activated by test cases, comparing total lines to those tested. It gauges how much of a program's source code is tested, aiming for higher coverage to reduce potential undetected bugs.

```
def checkNumber(num):
 if num > 0:
   return "Positive"
 elif num < 0:
   return "Negative"
```
Consider I have a test case that gives the input to the num variable as 10 and calls the function checkNumber. When it’s time to execute the if block, it runs line by line.

Now since the if block was entered, the program exits after the return statement. So the code checked for two lines of code out of four, which means 50% of the code. In simple words, I covered just half of the code in my program, hence my code coverage is 50%.

### Measuring Code Coverage
Code coverage is often represented as a percentage indicating the portion of code executed during testing. It can be assessed through various methods such as statement, branch, condition, and function coverage.

Above is an example of a conditional block to determine the coverage of the code. In testing terms, it is referred to as **branch coverage**, since a decision-making block becomes a root of a branch, where one branch means the condition returned true and the other one is false.

#### Statement coverage
The statement coverage in testing just verifies that all the executable lines of code have been implemented at least once.

General formula for calculating statement coverage:

_Statement coverage = (Number of executed statements / Total number of statements) * 100%_

#### Branch coverage
Branch coverage measures how well testing has covered possible paths in the code. Represented as a percentage, it indicates the proportion of branches executed during testing, with 100% implying all branches were tested.

_Branch coverage = (Number of branches executed / Total number of branches) * 100%_

#### Condition coverage
Condition coverage tracks how often each Boolean expression in the code evaluates to both true and false during testing. It's commonly used in control structures like if statements and loops.

_Condition coverage = (Number of conditions tested / Total number of conditions) * 100%_

#### Function coverage
Function coverage is a code coverage metric used to assess how test cases exercise the functions in the program when tested. It responds to the question, "How many functions in my code have been called at least once?"

The resulting ratio is then reported as a percentage, indicating the proportion of functions that have been tested. The formula for calculating function coverage is as follows:

_Function coverage = (Number of called functions / Total number of functions) * 100%_

If a function is not invoked during testing, it's probably not being exercised correctly and may have flaws.

### Why perform Code Coverage?
Code coverage is vital for code quality and reliability because it:

1. Highlights untested or unused code.
2. Evaluate the effectiveness of the test suite and pinpoint areas needing more testing.
3. Indicates potential areas with bugs, prioritizing which code segments to inspect first.
4. Helps identify and remove unnecessary "dead code."

### Advantages of Code Coverage
Code coverage is a valuable measure for assessing the quality and completeness of tests. Other advantages of code coverage include the following:

1. Increased software reliability as a result of extensive testing.
2. Shorter development cycles because developers can focus on testing critical portions of the code.
3. Easier debugging because code coverage can assist in detecting bug-infested portions of code.
4. Better code quality since code coverage identifies portions of code that may contain errors.
5. Increased confidence in the codebase, as code coverage identifies untested code sections

### Limitations of Code Coverage
Code coverage is a valuable metric for assessing code quality, but it has limitations:

1. High code coverage doesn't necessarily equate to high-quality code.
2. Perfect code coverage doesn't guarantee the absence of defects or that all functionalities are tested.
3. While code coverage measures code execution, it doesn't evaluate the test quality or overall system design.
4. Relying solely on code coverage might result in overlooking poorly written or inefficient tests.
5. Different tools may be required for various languages, adding to the implementation complexity.
6. Implementing code coverage can be time-intensive and costly.

### Code Coverage Tools:
1. **Coverage.Py for Python:** This tool provides details on the execution status of Python code components and can be installed using `pip install coverage`.
2. **JaCoCo for Java:** Operated by JUnit tests, it offers results in binary format.
3. **Testwell CTC++ for C++:** Downloadable from third-party sites, it stands out by indicating the execution frequency of each code line, rather than just its execution status.

## What is Test Coverage?
Test coverage plays a pivotal role in software creation and evaluation. It measures the extent of features tested, offering insights into potential risks. Unlike code coverage, which gauges the proportion of code executed in tests, test coverage focuses on feature validation. Ensuring comprehensive test coverage is vital for maintaining software quality and minimizing bugs.

While code coverage provides insights into the software's inner mechanics and guides improvement areas, test coverage offers a user-centric perspective.

### Why Test Coverage?
The objective of assessing test coverage is to confirm that vital business needs are adequately tested, leaving no substantial testing voids. It entails evaluating if all pertinent scenarios, including edge cases, are addressed and if the tests are sufficient enough to spot potential problems. This evaluation aids in pinpointing testing deficiencies and highlighting areas for software improvement.

### Measuring Test Coverage
Test coverage focuses on assessing the extent to which business requirements are examined, distinguishing it from code coverage. It offers a subjective insight into the thoroughness of tests in relation to business requirements rather than presenting a precise numerical value.

Key test coverage metrics include:

1. **Specification Coverage**: Verifies that every requirement of the software has undergone testing and is fulfilled.
2. **Product Coverage**: Confirms that every feature and component within the product has been scrutinized.
3. **Risk Coverage**: Highlights potential software vulnerabilities and checks if adequate tests have been conducted to address these concerns, minimizing potential challenges for users.
4. **Functional Coverage**: Assures that the software's functional criteria are comprehensively tested.
5. **Test Execution Coverage**: Offers insights into test outcomes, guaranteeing that the software undergoes rigorous testing prior to its official release.

### Benefits of Test Coverage

- Enhances software quality through comprehensive evaluation.
- Diminishes the risk of new defects and resolves existing ones.
- Easy to employ due to its black-box testing approach, requiring less code-specific knowledge.
- Pinpoints untested components, ensuring all product features are validated.
- Economizes on time and finances, reducing expensive bug corrections.
- Boosts confidence in the product by ensuring holistic examination.
- Facilitates quicker testing of code modifications, expediting release cycles.

### Drawbacks of Test Coverage

- Restricted scope, potentially missing issues like security risks or performance glitches.
- Might give an overconfident perception of code quality.
- The effectiveness hinges on the quality of the tests.
- Inadequate test coverage can be like navigating blindly, posing unnecessary risks.

### Test Coverage Tools:
1. **PyUnit:** A Python-based unit testing framework, PyUnit is renowned for facilitating the easy creation of test cases, tests, suites, and fixtures.
2. **JUnit for Java:** This open-source unit testing framework aids developers in tasks like writing, executing, and analyzing tests. Additionally, JUnit can handle user interface and regression testing.

## **Code Coverage vs. Test Coverage**

- **Quantitative vs. Qualitative**: Code coverage measures the percentage of lines of code tested, while test coverage gauges the depth and quality of tests.
  
- **Focus**: Code coverage looks at executed lines of code, while test coverage assesses the thoroughness of the entire testing activity.
  
- **Approach**: Code coverage employs a white-box testing method, examining the internal logic of the code. Test coverage, on the other hand, uses a black-box approach, considering the software's external functionality.
  
- **Usage**: Typically, code coverage is applied in unit tests, while test coverage is used in integration or system tests.

## **Code Coverage or Test Coverage?**

The choice between the two depends on specific goals. Both are crucial for a holistic testing approach.

Use code coverage during development to ensure every code line is tested. It's especially valuable for confirming specific code segments run during tests. On the other hand, when assessing the overall code efficacy, especially for performance, test coverage proves more beneficial, allowing swift issue resolution.

Each project's unique demands will guide the decision to employ either, or potentially both, types of coverage.

## Summary

Both code and test coverage are critical metrics for determining software correctness. Code coverage verifies and validates code quality by evaluating the number of codes executed while running automated tests. It ensures that all parts of the code have been tested and that there are no defects or bugs present.

In comparison, test coverage is a measure of the overall quality of the testing process. Both metrics are important, and deciding which to use depends on your specific scenario.



### References:

https://testsigma.com/blog/code-coverage-vs-test-coverage/
https://saucelabs.com/resources/blog/code-coverage-vs-test-coverage