Comprehending the intrinsic components that contribute to code quality is essential in the rapidly changing field of software development, where quality is paramount. Test coverage and code coverage are key measures for analyzing the accuracy and efficacy of software testing operations. While these metrics are frequently used in tandem, their purpose and application differ, typically providing distinct insights into distinguishing facets of software dependability and robustness.
Test coverage primarily defines the extent to which tests exercise a software application’s defined requirements and functions, further ensuring full validation of anticipated behaviors. On the other hand, code coverage assesses the amount to which the application’s actual source code was run during testing, briefly identifying sections that may have received insufficient inspection. These indicators give developers and stakeholders a comprehensive software quality perspective, directing efforts to create more robust and dependable programs.
Comprehending Code Coverage
This article delves into the subtleties of code coverage vs test coverage examining their differences and relevance in software testing, and collaborative efforts to create better software solutions.
The term ‘code coverage’ indicates the measure of a software application‘s source code that is run through testing. Essentially, It indicates a percentage of the code lines, branches, statements, or circumstances that the test suite has run through. The major objective of code coverage analysis is to find untested regions of the codebase and flag possible places where flaws can go unnoticed.
Code coverage is typically divided into many levels:
- Statement coverage aims to test every line of code at least once.
- Branch coverage guarantees that all potential branches, true or false, inside conditional expressions are carried out.
- Path coverage goes one step further by confirming that every potential path through the code has been examined and tested.
- Higher code coverage percentages indicate a more thorough test suite, which suggests a lower chance of detecting errors in the tested code pathways. However, obtaining 100% code coverage does not imply that the program is bug-free; it merely indicates that all lines or conditions were tested at least once.
Why is Code Coverage to be performed?
Code coverage is mainly performed at the unit testing level. Developers create unit tests so they can decide what tests to include in unit testing. At this point, you have questions, such as when performing, do more tests need to be added, or are there enough tests in the unit test suite?
As development progresses, new features are added to the codebase. The test code must be changed to stay updated with these changes. Testing standards established at the beginning of the project must also be maintained throughout release cycles. Code coverage ensures these standards are maintained so that only the optimal quality code is pushed to production.
A high percentage of code coverage results in lower chances of unidentified bugs. It is best to set a low rate of code coverage that must be achieved before testing in production to reduce the chances of bugs being detected later in development.
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Measuring Code Coverage
Measuring code coverage is simple because it’s a qualitative analysis. To calculate code coverage for a software component is:
Code Coverage (in %) = (Number of code lines that have been tested/Total number of lines of code in the corresponding software component) * 100. |
For instance – Consider testing your software component with 1000 lines of code. Out of the whole code component, if 700 lines of code get validated – the code coverage becomes 70%.
Criteria for Code Coverage
Various criteria are taken into consideration when performing code coverage analysis. The four main criteria to consider are:
1. Statement Coverage
The number of statements that are successfully executed in the program source code.
Statement Coverage = (Number of statements executed)/(Total Number of statements)*100. |
2. Branch Coverage
The number of decision control structures that are successfully executed in the program source code.
Decision Coverage = (Number of decision/branch outcomes exercised)/(Total number of decision outcomes in the source code)*100. |
3. Function coverage
The number of functions that are called and executed at least one time in the source code.
Function Coverage = (Number of functions called)/(Total number of functions)*100. |
4. Condition Coverage
The number of Boolean conditions executed in the conditional statement.
Condition Coverage = (Number of executed operands)/(Total Number of Operands)*100. |
Comprehending Test Coverage
Conversely, test coverage is more comprehensive than code coverage. It describes how much a software application’s requirements and functions are tested through test cases. Test coverage guarantees that testing and the code itself verify the anticipated behaviors and functions outlined in the requirements.
Test coverage in software testing involves numerous testing techniques like unit testing, cross-browser testing, integration testing, and acceptance testing. The measurement of test coverage is then done based on a number of features covered by the test code.
For instance, suppose you are writing test cases for a new user signing up. The test coverage should validate the test cases such as available username, invalid username, valid password set, and others.
There are several criteria by which test coverage may be characterized:
- Functional coverage guarantees that the program has undergone testing for all of its functional requirements.
- Non-functional testing primarily focuses on areas such as efficiency, security, and usability.
- Thanks to requirements coverage, at least one test case verifies every program need.
- Test coverage offers information on how well the program as a whole satisfies its intended purpose and specifications, in contrast to code coverage, which quantifies the thoroughness of code testing.
Measuring Test Coverage
Measuring test coverage is not the same as measuring code coverage because of the qualitative nature of test coverage. Various techniques listed below can evaluate the test coverage based on the application and industry.
1. Test coverage by functionality: This 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 application functionalities and start evaluating them one by one. Even though the features are tested by this technique, it definitely checks on the number of validated functionalities.
2. Test code coverage: This is one step ahead of the first technique. This technique uses a dedicated tool to return the code percentage implemented while testing, which is mostly automated. Again, in this case, the execution of code doesn’t guarantee the proper results for any of the tests. However, information about how much of the system code was executed helps determine the test coverage and later optimizes it.
3. Test coverage by User Interface: The main aspect that is related to the end-users is the UI of their application. So, UI elements of your application, like pop-ups, buttons, drop-downs, scroll bars, and others, need to be considered in 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 tested in detail.
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Code Coverage vs Test Coverage: Key Distinctions
Although both code coverage and test coverage seek to improve software quality, they differ greatly in their approaches and areas of focus:
- Focus: Code coverage prioritizes structural testing and tests the lines or pathways inside the codebase. Test coverage emphasizes functional and non-functional elements and guarantees thorough validation of software requirements.
- Scope: Code coverage indicates untested code routes by quantifying the amount of code that is executed during testing. Test coverage offers a more comprehensive view of software quality by guaranteeing that all required capabilities and requirements are sufficiently tested.
- Goal: Code coverage assists developers in identifying untested code regions, helping them create more successful test cases. Test coverage increases overall reliability by reassuring stakeholders that the product satisfies its intended functionality and quality standards.
Testing Exploratory: Improving Test Coverage
Exploratory testing, a hands-on method where testers examine the application without specific test cases, is a crucial component of thorough testing. This technique assists in identifying errors and usability problems that automated testing could overlook. By enabling testers to replicate real-world scenarios and user interactions, exploratory testing enhances overall test coverage and complements standard testing approaches.
Organizations may thus establish an equitable approach to testing that covers both anticipated and unforeseen aspects of the software by combining exploratory testing with formal test cases.
This ensures a more robust and comprehensive evaluation of the software’s performance and reliability.
Final Words on Ensuring Software Quality During the Development Process
Code coverage and test coverage constitute significant measures in software testing. However, they have different functions in guaranteeing the program’s quality. Code coverage measures the quantity of code run during testing, while test coverage ensures that functional and non-functional criteria are satisfied. When integrated, they provide a comprehensive view of program reliability and readiness for use.
Utilizing cutting-edge tools and test automation platforms like ACCELQ allows firms to optimize and improve the software testing process. With ACCELQ’s assistance for automated testing, smooth interaction with development pipelines, and extensive reporting features, teams may more effectively achieve better code and test coverage.
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