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Mason Collins

# Tool For Control Flow Of Cyclomatic Complexity [UPDATED]

For example, if source code contains no control flow statement then its cyclomatic complexity will be 1 and source code contains a single path in it. 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.

## Tool For Control Flow Of Cyclomatic Complexity

Mathematically, for a structured program, the directed graph inside control flow is the edge joining two basic blocks of the program as control may pass from first to second. So, cyclomatic complexity M would be defined as,

Cyclomatic Complexity in Software Testing is a testing metric used for measuring the complexity of a software program. It is a quantitative measure of independent paths in the source code of a software program. Cyclomatic complexity can be calculated by using control flow graphs or with respect to functions, modules, methods or classes within a software program.

Basis Path testing is one of White box technique and it guarantees to execute atleast one statement during testing. It checks each linearly independent path through the program, which means number test cases, will be equivalent to the cyclomatic complexity of the program.

Cyclomatic complexity can be calculated manually if the program is small. Automated tools need to be used if the program is very complex as this involves more flow graphs. Based on complexity number, team can conclude on the actions that need to be taken for measure.

Many tools are available for determining the complexity of the application. Some complexity calculation tools are used for specific technologies. Complexity can be found by the number of decision points in a program. The decision points are if, for, for-each, while, do, catch, case statements in a source code.

Thomas J. McCabe introduced Cyclomatic Complexity in 1976 as a way to guide programmers in writing methods that "are both testable and maintainable". At SonarSource, we believe Cyclomatic Complexity works very well for measuring testability, but not for maintainability. That's why we're introducing Cognitive Complexity, which you'll begin seeing in upcoming versions of our language analyzers. We've designed it to give you a good relative measure of how difficult the control flow of a method is to understand.

That's why we've formulated Cognitive Complexity, which attempts to put a number on how difficult the control flow of a method is to understand, and therefore to maintain.I'll get to some details in a minute, but first I'd like to talk a little more about the motivations. Obviously, the primary goal is to calculate a score that's an intuitively "fair" representation of maintainability. In doing so, however, we were very aware that if wemeasure it, you will try to improve it. And because of that, we want Cognitive Complexity to incent good, clean coding practices by incrementing for code constructs that take extra effort to understand, and by ignoring structures that make code easier to read.

As I mentioned, one of the biggest beefs with Cyclomatic Complexity has been its treatment of switch statements. Cognitive Complexity, on the other hand, only increments once for the entire switch structure, cases and all. Why? In short, because switches are easy, and Cognitive Complexity is about estimating how hard or easy control flow is to understand.On the other hand, Cognitive Complexity increments in a familiar way for the other control flow structures: for, while, do while, ternary operators, if/#if/#ifdef/..., else if/elsif/elif/..., and else, as well as for catch statements. Additionally, it increments for jumps to labels (goto, break, and continue) and for each level of control flow nesting:

A tool that generates control flow graphs from java source code.Which would then allow me to calculate cyclomatic complexity.(I don't mind if it does both but it must show the graph).

Please note I have tried "Control flow factory" by DrGarbage however this wouldn't directly me allow to calculate cyclomatic complexity ( I would have to first alter the graph and then calculate it - impractical for larger examples).

Just to make sure we are talking about the same thing, Cyclomatic Complexity is a metric that was introduced by Thomas McCabe in 1976 and aims at capturing the complexity of a method in a single number. Actually, the original publication talks about the more general term module, but today this typically means function or method for most languages. The metric itself is based on graph measures on the control flow graph of a method and describes the non-linearity of this graph. For structured programming (no goto) the metric is roughly equivalent to one plus the number of loops and if statements. For example, the Cyclomatic Complexity of the following Java method is 3:

Unless you prefer pen and paper for determining cyclomatic complexity, you will use one of the many existing tools for software metrics calculation out there. Unfortunately, the original paper is vague on some details of the metric, such as how to derive the control flow graph, and hence different implementations often result in different measured complexity values for the same code. For example, the following simple Java code snippet is reported with complexity 2 by the Eclipse Metrics Plugin, with 4 by GMetrics, and with complexity 5 by SonarQube:

Obviously, the complexity of the control flow graph is high and I would need many tests to completely test this method. But I feel fairly confident that I can understand and change that method without much effort even years after writing it (if I ever had to add a 13th month).

Cyclomatic complexity is computed using the control-flow graph of the program: the nodes of the graph correspond to indivisible groups of commands of a program, and a directed edge connects two nodes if the second command might be executed immediately after the first command. Cyclomatic complexity may also be applied to individual functions, modules, methods or classes within a program.

One testing strategy, called basis path testing by McCabe who first proposed it, is to test each linearly independent path through the program; in this case, the number of test cases will equal the cyclomatic complexity of the program.[1]

Mathematically, the cyclomatic complexity of a structured program[a] is defined with reference to the control-flow graph of the program, a directed graph containing the basic blocks of the program, with an edge between two basic blocks if control may pass from the first to the second. The complexity M is then defined as[2]

An alternative formulation is to use a graph in which each exit point is connected back to the entry point. In this case, the graph is strongly connected, and the cyclomatic complexity of the program is equal to the cyclomatic number of its graph (also known as the first Betti number), which is defined as[2]

McCabe showed that the cyclomatic complexity of any structured program with only one entry point and one exit point is equal to the number of decision points (i.e., "if" statements or conditional loops) contained in that program plus one. However, this is true only for decision points counted at the lowest, machine-level instructions.[4] Decisions involving compound predicates like those found in high-level languages like IF cond1 AND cond2 THEN ... should be counted in terms of predicate variables involved, i.e. in this example one should count two decision points, because at machine level it is equivalent to IF cond1 THEN IF cond2 THEN ....[2][5]

One of McCabe's original applications was to limit the complexity of routines during program development; he recommended that programmers should count the complexity of the modules they are developing, and split them into smaller modules whenever the cyclomatic complexity of the module exceeded 10.[2] This practice was adopted by the NIST Structured Testing methodology, with an observation that since McCabe's original publication, the figure of 10 had received substantial corroborating evidence, but that in some circumstances it may be appropriate to relax the restriction and permit modules with a complexity as high as 15. As the methodology acknowledged that there were occasional reasons for going beyond the agreed-upon limit, it phrased its recommendation as "For each module, either limit cyclomatic complexity to [the agreed-upon limit] or provide a written explanation of why the limit was exceeded."[9]

Section VI of McCabe's 1976 paper is concerned with determining what the control-flow graphs (CFGs) of non-structured programs look like in terms of their subgraphs, which McCabe identifies. (For details on that part see structured program theorem.) McCabe concludes that section by proposing a numerical measure of how close to the structured programming ideal a given program is, i.e. its "structuredness" using McCabe's neologism. McCabe called the measure he devised for this purpose essential complexity.[2]

One common testing strategy, espoused for example by the NIST Structured Testing methodology, is to use the cyclomatic complexity of a module to determine the number of white-box tests that are required to obtain sufficient coverage of the module. In almost all cases, according to such a methodology, a module should have at least as many tests as its cyclomatic complexity; in most cases, this number of tests is adequate to exercise all the relevant paths of the function.[9]

Neither of these cases exposes the bug. If, however, we use cyclomatic complexity to indicate the number of tests we require, the number increases to 3. We must therefore test one of the following paths:

A number of studies have investigated the correlation between McCabe's cyclomatic complexity number with the frequency of defects occurring in a function or method.[11] Some studies[12] find a positive correlation between cyclomatic complexity and defects: functions and methods that have the highest complexity tend to also contain the most defects. However, the correlation between cyclomatic complexity and program size (typically measured in lines of code) has been demonstrated many times. Les Hatton has claimed[13] that complexity has the same predictive ability as lines of code.Studies that controlled for program size (i.e., comparing modules that have different complexities but similar size) are generally less conclusive, with many finding no significant correlation, while others do find correlation. Some researchers who have studied the area question the validity of the methods used by the studies finding no correlation.[14] Although this relation is probably true, it isn't easily utilizable.[15] Since program size is not a controllable feature of commercial software, the usefulness of McCabes's number has been called to question.[11] The essence of this observation is that larger programs tend to be more complex and to have more defects. Reducing the cyclomatic complexity of code is not proven to reduce the number of errors or bugs in that code. International safety standards like ISO 26262, however, mandate coding guidelines that enforce low code complexity.[16]