A primer on code quality

What is Code Quality?

Code quality is the measure of how low or high the value of a specific set code is. While the definition of code quality is subjective, high-quality code can be clean, simple, efficient, and reliable code. The simpler the code is to read, the easier it is to understand and edit it. The more efficient the code, the faster it runs with fewer errors. Some other properties of code that contribute to high quality code are code clarity, complexity, safety, security, maintainability, testability, portability, reusability, and robustness. These code qualities define how a single unit of code can impact the overall quality of your codebase.

Code quality is a key aspect of software development. Regardless of the language used to write the code, the quality of the code impacts the quality of the end product, ultimately, the success of the organization.

Why is this important?

Maintaining high code quality is a crucial aspect for developers. Any poorly written code can lead to technical debt, performance issues, and security risks. The major challenge for development teams to deliver high code quality is the constantly changing codebases. The modern-day digital ecosystem is changing rapidly with evolving technologies as well as customer expectations. Therefore, there is a constant change in the codebases the developers work with. The development team adds, deletes, and changes existing code on a regular basis to improve the speed or update new features. However, these constant code changes often degrade the quality of code. This is where code quality metrics prove useful for developers.

 Tracking code quality metrics empowers development teams to analyze and find what makes code readable, understandable, and of sustainably high quality. And, high quality code means better software quality. And, high software quality means good business. This is where code quality can have a big impact, requiring developers to track code quality metrics.

How do you measure it?

There are metrics divided into quantitative and qualitative categories.

Qualitative Metrics

They are not measurable but more intuitive. They help in categorizing code as acceptable or rejectable. Qualitative metrics help you assess whether your development teams are adhering to coding standards, assigning meaningful names for objects, or implementing a maximum line width across a codebase, among other coding best practices. However, these metrics are highly subjective. For instance, some developers prefer longer variable names that help understand the purpose of the object, while others may feel comfortable using short names like Ord or Cust. So, this makes qualitative metrics more challenging to define. One best practice to define subjective quality metrics and enhance code quality is to perform regular code reviews.

Some of the key qualitative code quality metrics that you need to track are:

1. Readability

Readability is the most important code quality metric as it leads to higher levels of understanding of the code among other developers. It includes factors like clear and consistent naming conventions, proper indentation and formatting, meaningful comments, and logical code structure. Readable code is easier to maintain, debug, and collaborate on. Proper indentation, formatting, and spacing make the code more readable. This also makes the code structure more consistent and visible and eases debugging process. Add comments on the code where ever required, with concise explanations for each method. Also, use consistent naming styles like camelCase, PascalCase, and snake_case. The code readability can also be improved by reducing the level of nesting.

2. Reliability

Reliability is the code's ability to function without fail over a specific period of time. So, measuring code reliability can help you determine the success of your software or application. You can determine the reliability of your code by conducting static code analysis. This test identifies any defects or faults in your code. Then, you can make the required code changes to fix the errors and improve the code quality. A low defect count is imperative for developing a reliable codebase.

3. Portability

Portability metric measures how usable your code is in different environments. It shows how well other developers can use your code in other environments. You can ensure the portability of your code by regularly testing it on different platforms. Another best practice is to set the compiler warning levels as high as possible. Ensure to leverage two compilers. You can also improve portability by enforcing a coding standard.

4. Reusability

Reusability metric measures whether the existing code is reusable or repurposed for other programs or projects. Characteristics such as modularity or loose coupling make code easily reusable. You can measure the reusability of your code by the number of interdependencies it has. Interdependencies are the code elements that function properly when other elements perform properly. Conducting a static code analysis can help you find these interdependencies.

5. Testability

Testability metric measures how well the code supports various testing processes conducted on it. It depends on your ability to control, isolate, and automate tests. You can measure the testability of your code and the number of tests it takes to identify potential faults in the code. The size and the complexity level of the code impact the number of tests it takes to find any errors. Therefore, it is best to test at the code level, such as cyclomatic complexity, to improve testability. Some other best practices to improve testability are:

■ Conduct unit test first

■ Extract all non-testable code into wrapper classes

■ Leverage Inversion of Control / Dependency Injection

6. Maintainability

Code maintainability metric measures how easy it is to make changes to the code while keeping the risks associated with such changes as low as possible. It can be evaluated by the number of lines of code within the application. If these lines are more than the average number, then the maintainability is inferred to be low. Some of the best practices to improve maintainability are:

■ The code should be well-designed – it should be as simple as possible, easy to understand, easy to make changes, easy to test, and easy to operate

■ Refactor code

■ Document properly to help developers understand the code

■ Automate build to easily compile the code

■ Leverage automated testing to easily validate changes

7. Clarity

Clarity metric measures how clear the code is. A high-quality code should not be ambiguous. It should be clear enough to be easily understood by other developers without taking much time. Some of the best practices to improve code clarity are:

■ Ensure that your code has straightforward logic and flow-of-control

■ Leverage blank lines to segregate your code into logical sections

8. Efficiency

The Efficiency metric is the measure of the number of assets that are leveraged to build the code. It also takes into account the time taken to run the code. An efficient code should take less time to build and is easy to debug. Ultimately an efficient code should be on par with the defined requirements and specifications.

9. Extensibility

Extensibility metric measures how well your code can incorporate future changes and growth. Good extensibility indicates that your developers can easily add new features to code or change existing functionality without impacting the performance of the entire system. Leveraging concepts like loose coupling and separation of concerns can make your code more extensible.

10. Documentation

A quality code is defined as code that can be "used long term, can be carried across to future releases and products, without being considered as legacy code". To achieve this, you need documentation. A well-documented code enables other developers to understand it and use it, without much time and effort. Documentation ensures that code is readable as well as maintainable for any developer who deals with it at any point in time.

11. Scalability

Scalability assesses the ability of the code to handle increased workloads, data volumes, and user traffic. Scalable code is designed to efficiently utilize resources, handle concurrent requests, and scale horizontally or vertically as needed. It avoids performance bottlenecks, excessive resource consumption, and limitations on system growth.

12. Security

Security evaluates the resilience of the code against potential vulnerabilities and threats. It encompasses practices such as input validation, proper handling of sensitive data, protection against common attacks (e.g., SQL injection, cross-site scripting), and adherence to security standards and guidelines. Secure code mitigates risks and protects user data.

Quantitative Metrics

These can be defined with a numerical value that helps decide the viability of your code. These metrics require you to employ a formula as well as leverage certain algorithms that measure the code quality in terms of the level of complexity. Some of the key quantitative code quality metrics that you need to track are:

1.Lines of Code (LOC)

Lines of Code is a simple measure that counts the total number of lines in a codebase. While it can provide a rough estimation of code size and complexity, it should be used with caution, as lines of code alone do not necessarily indicate code quality or functionality.

2. Maintainability Index

The Maintainability Index quantifies the maintainability of code based on factors such as complexity, code duplication, and code size. It considers metrics like cyclomatic complexity, lines of code, and Halstead metrics to calculate an index score. Higher scores indicate better maintainability.

3. Code Coverage

Code Coverage measures the percentage of code that is exercised by automated tests. It indicates how thoroughly the codebase is tested and helps identify areas that may lack sufficient testing. Tools like JaCoCo, Istanbul, and PHPUnit can generate code coverage reports.

4. Code Duplication

Code Duplication measures the amount of duplicated code within a codebase. It identifies sections of code that are repeated and helps detect opportunities for refactoring and code reuse. Tools like Simian, PMD, and SonarQube can assist in identifying code duplication.

5. Defect Density

Defect Density calculates the average number of bugs or defects per unit of code, such as per line or per function. It provides insights into the overall quality of the codebase and can help prioritize areas for bug fixing and improvement.

6. Technical Debt

Technical Debt represents the accumulated cost of suboptimal or incomplete code that may require future refactoring or improvement. It can be measured in terms of estimated time or effort required to address the debt. Tools like SonarQube often provide metrics related to technical debt.

7. Coupling and Cohesion Metrics

Coupling metrics assess the interdependencies between modules or components, measuring the extent to which changes in one module can impact others. Cohesion metrics measure how closely related and focused the responsibilities within a module are. Low coupling and high cohesion are desirable for maintainable code.

8. Weighted Micro Function Points (WMFP)

Weighted Micro Function Points (WMFP) metric is the latest software sizing algorithm, that came as a successor to SOLID scientific methods. The metric parses source code and fragments it into micro functions. Then, the algorithm leverages these micro functions to generate several metrics displaying various levels of complexity. These metrics are then interpolated into a single rating, that reveals the complexity of the existing source code. Comments, arithmetic calculations, code structure, and flow control path are some of the metrics used to determine WMFP value.

9. Halstead Complexity Measures

The Halstead complexity measures, proposed by Maurice Halstead, quantify the complexity of a program based on the number of operators and operands used. The measures include:

Program Length (N): The total number of operator and operand occurrences in the code.

Program Vocabulary (n): The number of unique operator and operand types in the code.

Volume (V): A measure of the code's size and complexity based on N and n.

Difficulty (D): Represents the effort required to understand the code, taking into account V and n.

Effort (E): Estimates the time and resources needed to develop and maintain the code, based on D and V.

Time Required to Program (T): An estimate of the time required to write the code based on E.

10. Cyclomatic complexity

It measures the number of linearly independent paths through a program, indicating the complexity and potential difficulty of testing and maintaining the code. It helps identify areas where the code may be prone to errors or require additional attention. Cyclomatic complexity can be calculated using control flow graph analysis.

How Long Does It Take to Analyze Code Quality Metrics

Calculating and analyzing Code Quality Metrics manually is time-consuming and resource-intensive. Typically, a CI/CD environment contains more than 10-25 tools, making it quite challenging for your DevOps team to glean meaningful insights from individual tools. You need unified analytics with searchable logs to troubleshoot issues or identify redundancies or efficiencies. This is best accomplished with a platform that integrates data across tools to provide holistic reporting and dashboards, including everything from planning to production deployment and the embedded quality and security gates.

To sum up

• There are many tools and guidelines available in the market that enable you to track and assess code quality metrics.
• Despite leveraging many tools, there's always a scope that you miss to check the code before pushing it to source control. This is where integrating your tools into the CI/CD pipeline(link is external) proves important. This seamless integration ensures that your code quality checks are run on every commit to source control.
• Make your build to fail on certain issues and trigger an alert listing all the warnings and errors.
• Also, set up unit tests and run them in your CI/CD pipeline, so that your build will fail when the unit test results are negative.
• You can also fail your build when the code coverage falls below a threshold value.