It is used to measure the minimum effort and best areas of concentration for testing.
Considering the example above, each time an additional if-then-else statement is added, the number of possible paths grows by a factor of 2. As the program grows in this fashion, it quickly reaches the point where testing all of the paths becomes impractical. Another application of cyclomatic complexity is in determining the number of test cases that are necessary to achieve thorough test coverage of a particular module. This corresponds to the characterization of cyclomatic complexity as “number of loops plus number of components”.
But cyclomatic complexity also has significant ramifications because of its impact on unit tests. Specifically, difficulty of testing grows proportionately with cyclomatic complexity. If a method has cyclomatic complexity of 25, this means that you would need to write 25 tests before you addressed each path through the code. He did, however, give it an intimidating-sounding name. I mean, complexity makes sense, but what does “cyclomatic” mean, exactly? Well, “cyclomatic number” serves as an alias for something more commonly called circuit rank.
With that in mind, I’d like to focus specifically on cyclomatic complexity today. But let’s take a look in great detail to avoid misconceptions and clear up any hazy areas. Some of my scripts had 500+ complexity and had to spend a lot of time to tune it down to 100. I learned a lot from that, however it was showing everywhere so I decided to frequently turn it off. If you wanted to get 100% code coverage you would need to cover every possible route, this is what the complexity of trying to convey.
Circuit rank measures the number of independent cycles within a cyclic graph. So I suppose he coined the neologism “cyclomatic complexity” by borrowing a relatively obscure discrete math concept for path independence and applying it to code complexity. Code coverage is becoming an integral part of the development cycle. Cyclomatic complexity ultimately effects the amount of different unit tests you will need to write for a given piece of code. Additional routes through a method will require an additional test to be written, and an additional place your code could fall over, or bugs could crop up.
The more branches there are within a particular function, the more difficult it is to maintain a mental model of its operation. The metric is roughly equivalent to one plus the number of loops and if statements. The name Cyclomatic Complexity presents some confusion, as this metric does not only count cycles in the program.
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This is actually surprisingly difficult because not all sources agree on the exact method for computing it. Make all posts by chris_bertrand less visible chris_bertrand consistently posts content that violates DEV Community’s code of conduct because it is harassing, offensive or spammy. Once unsuspended, chris_bertrand will be able to comment and publish posts again. Once suspended, chris_bertrand will not be able to comment or publish posts until their suspension is removed. It will become hidden in your post, but will still be visible via the comment’s permalink. Refactoring and improving your codebase is imperative to keeping a clean and concise environment.
These sometimes crop up during development and then don’t get removed. One common example you may find in your code base goes like this. Flow Graph notation for a program defines several nodes connected through the edges.
What Is Cyclomatic Complexity? Measuring Code Quality
Since it’s just perfectly linear code, the number of nodes will cancel out the number of edges, giving a cyclomatic complexity of one. However, the relationship of cyclomatic complexity with cognitive complexity isn’t always that clear-cut. It’s possible to have methods with high cyclomatic complexity that are easy to understand. https://baravik.org/topic/921/ The higher the cyclomatic complexity, the more possible ramifications there are inside a given code excerpt. For instance, a function with a cyclomatic complexity of 1 will always go through the same execution path. Conversely, a function with a cyclomatic complexity value of 8 will have eight possible execution paths.
For convenience, you can use the search bar to simplify and speed up the search process. Hence, the cyclomatic number also equals the number of edges not in a maximal spanning forest of a graph. Since the number of edges in a maximal spanning forest of a graph is equal to the number of vertices minus the number of components, the formula above for the cyclomatic number follows. So, complex code that suffers a lot of churn—frequent changes by the team—represents more risk of defects. By reducing the cyclomatic complexity—and, ideally, the code churn as well—you’ll be mitigating those risks.
This corresponds to the characterization of cyclomatic complexity as “number of loops plus number of components”. As complexity has calculated as 3, three test cases are necessary to the complete path coverage for the above example. Writing code that simply duplicates functionality that your language’s standard library or your framework already provides is a sure way to increase complexity unnecessarily.
Cyclomatic Complexity really is just a scary buzzword. In fact it’s a measure of code complexity used in software development to point out more complex parts of code . You can calculate it using the E-N+2P formula, but I would suggest you have this calculated automatically by a plugin. I have heard of a rule of thumb that you should strive to keep the CC below 5 to maintain good readability and maintainability of your code. Cyclomatic complexity measures the number of times you must execute a block of code with varying parameters in order to execute every path through that block. A higher count is bad because it increases the chances for logical errors escaping your testing strategy.
Similarly, if the source code contains one if condition then cyclomatic complexity will be 2 because there will be two paths one for true and the other for false. Cyclomatic complexity measures the number of execution paths through code, but excessive return statements can make a function harder for a human to follow because control flow jumps around. If one considers the control flow graph as a 1-dimensional CW complex called , then the fundamental group of will me . The fundamental group counts how many loops there are through the graph, up to homotopy, and hence aligns with what we would intuitively expect. Consider the control flow graph of your function, with an additional edge running from the exit to the entrance.
Lower Cyclomatic Complexity = Better Code
As you’ll soon see, cyclomatic complexity is easy to understand and calculate. This is great since it has important implications for code maintainability and testing. There are metrics you can use to have meaningful conversations about code, and one of the most valuable is cyclomatic complexity. The control-flow graph of the source code above; the red circle is the entry point of the function, and the blue circle is the exit point.
There are many reasons why it’s a good idea to remove obsolete—i.e., dead—code from your application. For our context, it suffices to say that that’s a “free” way to bring code coverage up and cyclomatic complexity down. However, generally speaking, we can say that having a too high cyclomatic complexity is either a symptom of problems with the codebase or a potential cause of future problems. Let’s cover some of the reasons why you’d want to reduce it in more detail. Since it has a single statement, it’s easy to see its cyclomatic complexity is 1.
- Since the number of edges in a maximal spanning forest of a graph is equal to the number of vertices minus the number of components, the formula above for the cyclomatic number follows.
- The control-flow graph of the source code above; the red circle is the entry point of the function, and the blue circle is the exit point.
- The cyclomatic complexity of a function measures the number of possible paths through that function’s code.
- Cyclomatic complexity is a metric that indicates the possible number of paths inside a code artifact, e.g., a function, class, or whole program.
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One thing this method would seem to miss is that additional branches sometimes multiply the number of paths and sometimes don’t. Passionate about testing, functional programming, and pretty graphs. Code is legacy the moment it is written, so your refactoring might be obsolete during the next round of development. However, if we simply remove the else statement and move the default message to the declaration we remove 1 complexity point straight away.
An even subgraph of a graph is one where every vertex is incident with an even number of edges; such subgraphs are unions of cycles and isolated vertices. In the following, even subgraphs will be identified with their edge sets, which is equivalent to only considering those even subgraphs which contain all vertices of the full graph. Will be equal to the number of programs in question, as each subprogram will appear as a disconnected subset of the graph. It is the measure of the program’s control complexity and not the data complexity. Admittedly, these shortcomings of cyclomatic complexity present real challenges to its use. Hi Dustin, nested branches are definitely classed as more complex, have a look at your examples run through the Code Metrics extension in VSCode.
If you find your code is running smoothly, not causing you or your customers any issues, then don’t change it because a code metric is telling you it’s wrong. Even spell check doesn’t recognise the word, but it’s a super useful software metric for understanding how your software works. “relative” means the path must begin and end at an entry or exit point. Π – s + 2where π is the number of decision points in the program, and s is the number of exit points.
Unfortunately, it is not always practical to test all possible paths through a program. Considering the example above, each time an additional if-then-else statement is added, the number of possible paths doubles. As the program grew in this fashion, it would quickly reach the point where testing all of the paths was impractical. A better name for popular usage would be Conditional Complexity, as “it has been found to be more convenient to count conditions instead of predicates when calculating complexity”. Cyclometic complexity is also known as the number of FAN IN and FAN OUT in a program.
With a simple example in the books, let’s generalize a bit. Introducing the conditional created additional complexity by adding another path through the code. But as you can deduce, we have other means of adding complexity.
As always, let us know what you think in the comments below. Well this is where cyclomatic complexity enters the frame. LinearB is here to help you spot where there might be problems in your process. If you’d like to learn more and improve your own code quality, check out our five-step recipe for improvement.
But the same thing has come to apply for non-technical stakeholders as well. They concern themselves with cyclomatic complexity precisely because they, too, care about defect likelihood. Cyclomatic complexity matters mainly because it serves as a way to quantity complexity in your code. And that matters because complexity translates directly to risk, a concern of interest both to the business and to developers. Complexity of code elements, both in terms of size and paths through the code, correlate with defects. And this makes sense since each of those introduces a new path through the code.