Code quality and readability are important aspects of programming that affect the maintainability, reliability, and efficiency of your software. But how can you measure them objectively and consistently? In this article, you will learn about some common metrics and tools that can help you assess and improve the quality and readability of your code.
One of the simplest ways to measure the quality and readability of your code is to follow a consistent syntax and style. Syntax refers to the rules and conventions of the programming language, such as indentation, punctuation, and naming. Style refers to the choices and preferences of the programmer, such as comments, spacing, and formatting. By adhering to a syntax and style guide, you can avoid common errors, reduce ambiguity, and enhance readability. You can use tools such as linters, formatters, and code editors to check and enforce syntax and style rules automatically.
Consistent coding standards establish guidelines for formatting, naming, and structure, enhancing code readability and reducing errors. They facilitate collaboration by providing a shared language and automated enforcement with tools like linters and formatters. This ensures code remains cohesive, scalable, and maintainable, even as projects evolve.
Syntax is the foundation. Adherence to language-specific rules ensures that code is interpretable by compilers or interpreters. Tools like linters can automate syntax checks, saving time and preventing common errors.
Another way to measure the quality and readability of your code is to analyze its complexity and length. Complexity refers to the number and interdependence of the elements and paths in your code, such as variables, functions, loops, and branches. Length refers to the number of lines or characters in your code. High complexity and length can indicate that your code is difficult to understand, test, and debug. You can use tools such as code analyzers, metrics, and visualizers to measure and reduce complexity and length. Some common metrics are cyclomatic complexity, lines of code, and code coverage.
Some tools to help with static code analysis can improve code quality by detecting code smells, security hotspots or code that has issues. It can also assist in finding code that has higher cognitive complexity.
Use SAST and DAST tools in the build pipeline to identify some commonly made mistakes and improvise the coding standards to not repeat them again. Cyclomatic and Cognitive complexity of a functions can be easily avoided if they are frequently monitored using tools.
A third way to measure the quality and readability of your code is to evaluate its modularity and cohesion. Modularity refers to the degree to which your code is divided into separate and independent units, such as modules, classes, or functions. Cohesion refers to the degree to which the elements within a unit are related and focused on a single purpose. High modularity and cohesion can indicate that your code is easy to reuse, extend, and maintain. You can use tools such as refactoring, design patterns, and dependency analysis to increase modularity and cohesion.
Importance of having methods/definitions to break down the business logic into understandable pieces of code helps in readability.
A fourth way to measure the quality and readability of your code is to review its documentation and comments. Documentation refers to the external information that explains the purpose, functionality, and usage of your code, such as user manuals, API references, or tutorials. Comments refer to the internal information that clarifies the logic, assumptions, and decisions of your code, such as inline comments, docstrings, or annotations. Good documentation and comments can help you and others understand, use, and modify your code. You can use tools such as documentation generators, comment standards, and code reviews to improve documentation and comments.
Documentation is the roadmap to your code. It should clearly explain what the code does, why certain decisions were made, and how to use it. Remember, code tells you how, documentation tells you why.
Documentation is the key for maintaining and re-using your code. There are 3 types of comments: 1. Single line comment 2. Multi-line comment 3. Documentation comment Prefer using the documentation comments which enhances the readability of the code, the comments should be precise and should help other developers to understand why the function should be re-used.
A fifth way to measure the quality and readability of your code is to use readability and quality scores. Readability scores are numerical values that estimate how easy or difficult your code is to read, based on factors such as vocabulary, structure, and readability formulas. Quality scores are numerical values that estimate how good or bad your code is, based on factors such as defects, performance, and security. Readability and quality scores can provide you with a quick and objective feedback on your code. You can use tools such as readability calculators, quality checkers, and code graders to obtain readability and quality scores.
Readable code is easily understood by humans, not just machines. It follows naming conventions, has a consistent style, and includes comments where necessary to explain complex logic.
A sixth way to measure the quality and readability of your code is to follow best practices and principles. Best practices are guidelines and recommendations that are widely accepted and proven to produce high-quality and readable code, such as naming conventions, coding standards, and testing strategies. Principles are fundamental concepts and values that guide your design and development decisions, such as simplicity, clarity, and modularity. By following best practices and principles, you can ensure that your code meets the expectations and requirements of your project, team, and users. You can use tools such as books, blogs, and courses to learn and apply best practices and principles.
Follow below and create guidelines around these 1. Meaningful variables and function names 2. Consistent naming conventions 3. Effective use of comments and whitespaces 4. Follow strategies like KISS (Keep It Simple Stupid) and DRY (Don't Repeat Yourself) 5. Low Coupling and High Cohesion 6. Adequate log messages and logging levels 7. Exception handling and failover mechanisms
It’s noteworthy to follow naming conventions, proper code style( like PEP-8 for Python). It might look trivial but it adds so much value in terms of readability, understandability to the code.