In software development, code complexity means how simple or complex it is to comprehend as well as update and expand. It has been identified that it serves an important purpose in defining the quality of a software product. Cohesion is something that each developer faces at an early stage of their development career, and the way they handle it has a serious impact on the longevity and efficiency of the developed application.
This blog will look at what code complexity entails, how it is measured, measures that can be taken to minimize it and how, with data abstraction, we can manage complexity. It will also be quite useful with developers who are seeking ways of enhancing the standard of their code base and overall productivity.
What is Code Complexity?
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In its broadest sense, code complexity is the measure of the ease or otherwise with which a software program can be read, understood and modified. A higher increase in the level of code conciseness results in more time to produce a desired piece of code and in higher costs due to more difficulties in code having bugs, furthering, and maintenance. There are different types of difficulties, for example, many conditions, nested loops, huge functions, and non-optimized code.
There are two main types of code complexity that developers should be aware of:
- Structural Complexity:
This type of complexity can be attributed to the organization and structure of code. Large and non-refined functions or classes usually cause this, low quality modularity and use and poor use of design patterns. Complexity of structure is usually found when there is much copy and paste or when there is not enough abstraction. The more complex the structure of the code is, the more difficult it will be to read and tweak, and therefore the more problematic will be it’s maintainability.
- Logical Complexity:
Logical interdependence refers to the connectivity of the logical elements of a program where it depends: Error-proneness is influenced by different flows of logic within the code, like decision-making flows (conditionals, loops, branching). The number of decisions that a program makes increases the amount of code that is written in that program. This is evident in applications that have several enclosed loops, many branches of if and else statements or Boolean expressions. The more complex the inflicted logic is, the less possible it is to predict or even experiment with the patterns of the software.
Indeed, all code has a certain level of complication. Still, a good developer goes to great lengths to ensure that the code is as simple as possible and as complex as necessary – meaning that a developer utilizes principles such as modularity and abstractions to keep the code tame.
How to Measure Code Complexity
It is, therefore, important to quantify it in order to determine where optimization can be done and to ensure that the code can still be developed without demoralizing the engineers. This is done through supposed metrics that afford an understanding of the different characteristics of the code. Now, it will be useful to discuss some of the most widespread approaches and instruments used to measure complexity.
Key Metrics for Measuring Code Complexity
Cyclomatic Complexity:
Cyclomatic complexity, as originated with Thomas McCabe, is a measurement that targets the number of free paths in the source codes of a program. It is a direct indication of how much the decision-making of the code is complicated. Cyclomatic complexity is the number of paths in source code and higher cyclomatic complexity means complex logic which is difficult to access, manage, and inspect for testing as well.
The formula for cyclomatic complexity is:
Cyclomatic Complexity=E−N+2P
Where:
- E is the number of edges in the control flow graph,
- N is the number of nodes,
- PPP is the number of connected components.
Example:
A simple “if-else” statement will have a cyclomatic complexity of 1, while more complex logic involving multiple conditions and branches could have a cyclomatic complexity of 3 or more.
Halstead Metrics:
Halstead measures the level of complexity in a code by the number of operators and operands. The main put it is quite simple: the more distinct operators and operands within the code, the harder it is. This metric helps developers to know in advance how easy or how hard it will be to write, comprehend and debug the code.
Halstead metrics include:
- Program Volume: Spent a length of measure of size that can be determined from the number of operators of arity zero and nonzero arity, respectively.
- Difficulty: Explains how difficult it would be to comprehend the program; it is arrived at by adding operators and operands.
- Effort: Suggests the level of thinking needed to comprehend the program in terms of reading.
Maintainability Index (MI):
Together with lines of code, Halstead metrics and cyclomatic complexity, the Maintainability Index gives an average that characterizes how easy it is to maintain a given software product. The MI assists the developers in determining the extent to which the code is easy to modify or when modification will be costly.
Tools for Measuring Code Complexity
There are several tools available to automatically measure the complexity of your code and provide actionable feedback:
- SonarQube: A widely-used open source that offers many characteristics of the code’s quality, such as the code complexity. SonarQube provides you with a graphical view of your source code and also about which areas you need to focus on.
- CodeClimate: It identifies your encoded material and offers a simple-to-understand report on several quality measures like difficulty level, redundancy and health of the code, among others.
- Visual Studio Metrics Tool: Developers using Visual Studio can use this built-in tool to perform cyclomatic complexity measurement and project maintainability tracking at the file and page level.
How to Reduce the Complexity of Code
However, decreasing it is as significant an endeavor as is a measurement of complexity for the enhancement of several qualities of the software as well as its sustainment of those qualities in the long run. Reducing your code complexity means your code is easy to read, debug and modify. Here are some strategies to reduce code complexity:
1. Refactor Code Regularly
Refactoring is the reorganization of the program’s code without altering its external characteristics. That is why it is recommended to refactor from time to time in order to decrease the system’s complexity and increase its readability.
Key refactoring steps include:
- Dividing a broad flow of control or a method into subroutines or individual and smaller functions.
- Redundancy is best gotten rid of through the creation of functions that can be utilized severally.
- Removal of compounders as well as other conditions that you find challenging to manage.
2. Adopt Modular Programming
The fact is that one of the most effective ways to control the growth of program size and the variety of hierarchies is not to write complex codes in the first place but instead create a set of autonomous modules or functions. Modularity enables you to center your attention on functions or features in small units that are easy to develop and test.
3. Avoid Deep Nesting
Extra indentation of many levels is not favorable to comprehensiveness simply because it can give rise to the densification of the code. Elimination of nesting enhances the format of codes and, at the same time, reduces risks of involving error-prone nests. Consistent use of early exits or “guard clauses instead of deeply layered ‘if-else’ conditions is recommended.”
Why Managing Code Complexity is Important
The dark side of phrasing is not just about producing cleaner code – it has a direct correlation with success. Here’s why it is crucial to manage complexity effectively:
- Improved Maintainability: The code that is easy to read and structured is easier to modify and update. This is critical since the software is not permanently static, and often, there is a need to add new features or even a fix.
- Fewer Bugs and Errors: It is for this reason that complex codes are likely to be exposed to a lot of errors. This reduces the probability of adding new bugs and, at the same time, reduces the complexity of work on previous bugs by making them more conspicuous.
- Better Collaboration: Through clean and simple code, work becomes easier for the whole team engaged in the project. As such, new developers can work on a new part of the project and have a better start; the coordination risk is also lowered because clarity and accuracy are achieved.
- Performance Optimization: Simplifying a program by taking out unnecessary code can still make the program run faster because these operating paths slow the program down.
Conclusion
The use of code complexity as a measure is unavoidable in software development processes, but measures can be put in place that reduce excessively complex code usage. But measuring complexity through various parameters such as cyclomatic complexity or Halstead metrics ratios, helps them identify those areas and get started with refactoring. Some ways, like refactoring, the use of modular programming, and avoiding excessive nesting, allow for simplicity, thereby simplifying the maintenance and expansion of the code. Moreover, data abstraction is one of the most important techniques for solving complicities. It allows specifying details of implementation and, at the same time, presenting only the needed interfaces.
