What is a branch in code coverage for JavaScript unit testing

When a test suite is run, code coverage records which lines of code were hit. Line coverage, thus, is the total number of lines run divided by the number of lines in the codebase. It helps in validating all the branches in the code making sure that no branch leads to abnormal behavior of the application. There are also some sorts of defects which are affected by such tools.

branch coverage

It helps to measure fractions of independent code segments and to find out sections having no branches. That’s why there are branch coverage many different methods of reporting this metric. All these methods focus on covering the most important combinations.

Deciphering Branch Coverage: A Comparative Analysis with Similar Metrics

Decision or branch coverage is a measure of branches that have been evaluated to both true and false in testing. It is a sequence of execution of conditional statements performed in a specific order. So basically Path Coverage collects information about in which order the consecutive statements are executed, the branches that are examined and how logical conditions evaluated during simulation. The first time you run your coverage tool you might find that you have a fairly low percentage of coverage. If you’re just getting started with testing it’s a normal situation to be in and you shouldn’t feel the pressure to reach 80% coverage right away. Branch Coverage process is an indispensable test coverage technique that allows the programmer and the tester to get better visibility on what is expected and what is achieved so far.

Indicates how many functions in the source code were reached at least once during execution. Complete, 100% function coverage is obtained if all functions are reached at least once. Coverage statistics for the project and all analyzed files and functions will be displayed in the Coverage view. If no coverage is shown after test execution, check that 1) coverage was enabled and 2) you are displaying the type of coverage you configured C/C++test to calculate. You can display the exact coverage of branches in ABAP source code in the graphical display of the Coverage API. Generate a code coverage report and save it to the specified directory.

What is Branch Coverage or Decision Coverage? Its advantages and disadvantages

Branch coverage also helps you detect potential bugs, errors, or vulnerabilities that might occur when your code encounters unexpected or invalid inputs or states. Branch coverage is a metric that measures how well your test cases cover the different branches or paths of your code. It is a type of code coverage that helps you identify gaps or redundancies in your test suite and improve its quality and effectiveness. In this article, we will explore some best practices for achieving high branch coverage in test engineering. Generally in any software, if we look at the source code, there will be a wide variety of elements like operators, functions, looping, exceptional handlers, etc. Based on the input to the program, some of the code statements may not be executed.

  • When a tested file is opened in the editor, C/C++test will use green highlights to indicate which code was cover ed and pink highlights to indicate which code was not covered.
  • Create more test cases to address those specific circumstances if any branches are still untested.
  • To be able to use this feature, enable the Tracing mode and Track per test coverage options for the current run/debug configuration in the Code Coverage area.
  • Edge coverage reports which branches or code decision points were executed to complete the test.

If we were to draw the path taken by Test 2_2, it would be a straight line from the read statement down the False exit and through the ENDIF. We could also have chosen other numbers to achieve either the True or False outcomes. Branch coverage will flag this code as not fully covered because of the missing
jump from line 2 to line 4. This is the basic code structure where we took two variables X and Y and two conditions. If the first condition is true, then print “Large” and if it is false, then go to the next condition.

3.2 Statement Coverage via Hardware Monitoring

At 500 thousand, the overhead improves by 4.3%, and at 1 million CPU cycles, the time overhead improves by 4.5% on average. However, if the code is reformatted, as shown below, statement coverage would be 66%. The goal of this technique is to cover all the statements at least once by executing the program. Branch coverage and Statement Coverage are form of white box testing techniques. This means that our test cases proved that a and b independently affect the value of the complete decision.

As a result, obtaining 100% branch coverage can be difficult and might call for a large number of test cases. The time overhead of execution compared to native execution time is shown in Fig. Overall, the time overhead of using the instructions retired mechanism is less than that when using CPU cycles.


So, in short, we can say that branch coverage is a subset of code coverage. It’s a more specialized version of code coverage that focuses on a specific aspect—i.e., ensuring that every branch or path is tested. Well, code coverage is the overall metric that refers to the ratio of the codebase that is currently exercised by tests.

A second run of our coverage tool will now show that 100% of the source is covered thanks to our two console.log() statements at the bottom. In the trivial script below, we have a Javascript function checking whether or not an argument is a multiple of 10. We’ll use that function later to check whether or not 100 is a multiple of 10. It’ll help understand the difference between the function coverage and branch coverage.

Code Coverage Tutorial: Branch, Statement & Decision Testing

In the above code, 71.5% statement coverage is achieved by test case #1. Execution paths resulting from special situations such as signals or exceptions thrown from functions called are not included in the set of possible execution paths. If such paths actually occur in the run time, they are counted and reported as “unexpected” ones. Indicates how many defined function or method calls were executed at program runtime. Complete, 100% call coverage is obtained if all functions or methods calls were executed.

branch coverage

To achieve the goal of exploiting hardware mechanisms for statement coverage, two common hardware mechanisms that can be used for sampling are examined. After selecting a mechanism, the time and code growth overheads that sampling incurs are experimentally evaluated and the level of statement coverage that can be observed during sampling is analyzed. Finally, additional information that can be obtained while monitoring test execution is demonstrated and discussed with regard to its potential usage within future work. The experimental results show that up to 79% of the statement coverage reported by instrumentation can be reported with lesser time overhead than instrumentation.

Types of Black Box

Only caring about statement coverage can give teams a false sense of security when it comes to the comprehensiveness of their tests. As you’ll soon see, branch coverage is more nuanced than other metrics. By only tracking the other metric, a team can have an unjustified degree of confidence in their code, and important defects might go unnoticed until they manifest in production. The most thorough of these metrics, Path Coverage aims to test every potential path through the code, including every possible arrangement of branches and decision points. For both of these expressions, it turns out that our test case is evaluating only 1 out of 2 possible cases.