Code Complexity Metrics: How to Use Them to Enhance Software Maintainability

Nov 30, 2024 Reading time : 5 min

As a code developer, I often come across monstrous codebases that are just difficult to maintain and are out of control. Have you experienced the same thing? And what’s the best solution for this? I recently stumbled upon something that might be exactly what we need, and that is code complexity metrics.

A research paper said, “The code complexity metric is one of the evaluation methods that use some of the internal variables or characteristics of software to know how they affect the quality.”(ResearchGate: The Correlation among Software Complexity Metrics with Case Study).

In this post, I will tell you about code complexity metrics: and how to use them to enhance software maintainability. If you are a part of this industry, then this is a must article for you. Pay attention.

Grasping Cyclomatic Complexity

Cyclomatic complexity is an important metrics that analyzes different codes with the help of nodes which represent individual statements and edges which indicates control flow. This ensures that a code is running smoothly.

By identifying several linearly independent paths, offering insights and control flow, this metric can be calculated. If you can understand cyclomatic complexity, you’ll be able to identify those parts of the codes that are complex and can be simplified.

This makes a code more manageable and easy to test, apart from that if there are any errors or potential issues those can be dealt with before it could create any problems.

Advantages of Leveraging Cyclomatic Complexity

There are several benefits to leveraging cyclomatic complexity, one of the main being figuring out the maintainability issue that is occurring in the code. Developers can pinpoint those areas that are creating problems or are hard to maintain by evaluating the difficulty level.

Such awareness helps with increasing testability, reducing any potential error, and timely interventions. Codes become more straightforward with lower cyclomatic structures, which makes it easier to update and manage over time.

These estimates open up a proactive strategy, which is essential for robust software that is simplified to work with. You might think this must be it, right? But no. It can easily detect bugs, integrate new features, and simplify all the layers.

Contrasting Cyclomatic Complexity with Other Metrics

There are different metrics available for developers and some of them are given below:

Halstead complexity: This focuses on types and frequencies of the operator/operands in the code. You may ask why is it useful, well it provides software’s computational logistical insights.
Lines of Code (LOC): If you want to know the number of code lines and want a straightforward but limited view of code size, this is your savior.

Basics of cyclomatic complexity are the most useful as they enable you to access the most logical paths and potential test cases. Halstead and LOC offer diverse evaluations that are aligned with potential error rates, maintainability, and code readability.

Implementing Complexity Metrics in Software Development

Here are a few steps that you can follow to implement complexity metrics in software development:

Establish a Baseline for Complexity Levels: Start by determining the acceptable degree of detail for your project. Take reference from past projects and mark the benchmarks.
Monitor Code Regularly: Since there are several tools available, keep checking the length of your code. Also, don’t forget to conduct routine code reviews.
Identify and Address Problematic Aspects: In case you find any errors or complexity, make sure to simplify them so they can be manageable and scalable.
Educate The Development Team: Your team needs to be well-educated and trained when it comes to handling different obstacles.
Incorporate Metrics into the Workflow: Do regular depth analysis and based on the data make decisions and prioritize technical debate resolution.

Tools for Evaluating Code Complexity

You might not know, but there are several tools for evaluating code complexity, and some of the best ones I’m mentioning below:

SonarQube: This supports a wide range of programming languages along with various crafting environments. If we talk about its key features, it offers a comprehensive analysis of all kinds of metrics.
CodeClimate: If you were looking for something that can easily integrate with CI/CD pipelines, this is perfect. You can also monitor code quality continuously. You are also met with a user-friendly interface, robust analysis, and detailed reports.
Pylint (for Python): Specially designed for Python developers, this tool improves code readability, evaluates code complexity, detects errors, and enforces coding standards.
CodeMetrics (for Visual Studio Code): For quick assessment, this is the best tool that you can find. It’s lightweight and doesn’t leave a development space while providing insights.

I’ve told you about the tools, but before picking any you have to consider everything. Is the software going to be suitable or compatible with your current project? Does it support the language, or will it integrate seamlessly? Think about these things first and then jump to the conclusion.

FUN FACT
Google has the biggest codebase in the world, with a staggering 2 billion lines of code stored in a single code repository!

Summary and Future Prospects of Complexity Metrics

So, this is the end of the article, and by now you must’ve understood that complexity metrics are essential for easy maintenance and high-quality software. You can spot different problems before they can turn into any major issue.

With time, these metrics are going to become more valuable, and in the future AI and machine learning will be used for better accuracy and predict potential threats. If you are a developer, you should incorporate these metrics as it could save you time and resources.