Speed has become the new currency in this fast-paced digital world. Customers expect services to be delivered at high speed and businesses that meet this demand have a significant edge in the market. And DevOps has empowered businesses to deliver software at speed. However, this speed should not come at the cost of quality. The code has to perform as per the business expectations. Random defects, obtuse errors, and poor user experience are detrimental to businesses that aspire to stay competitive in the market. By ensuring high code quality, businesses can improve their bottom line, provide a better experience for their customers, and thrive in the market. So, how can you ensure the high quality of your code? This is where Code Quality Metrics come in.
In this blog, we shall discuss the importance of code quality metrics, why developers need to track them, and how you can improve code quality, among other things. Let’s start with code quality:
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. So, what exactly 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. So how do you determine code quality? This is where Code Quality Metrics come in. These metrics help you gain a clear picture of your code health, understand the gaps, and take necessary remediation steps to improve the quality. These metrics are categorized into two groups – Quantitative Metrics and Qualitative Metrics.
Before we delve deep into what it is all about Code Quality Metrics, let’s take a step back and look at the 13 Key Metrics That You Need to Track to Tap the Full Potential of DevOps.
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.
Here are the top 7 code quality metrics that every developer must track to improve the ROI of the software and ultimately the profitability of the business:
The most effective code quality metrics are those that help identify the errors and bugs in the code. The common types of metrics for code quality evolution are:
Qualitative Metrics are more intuitive and cannot be expressed in numbers. They are not measurable. These metrics 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:
Readability is the most important code quality metric as it leads to higher levels of understanding of the code among other developers. Your code should be easy to read and understand. Because code that's easy to read or understand is easy to improve. 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.
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.
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.
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.
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:
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:
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:
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.
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.
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.
Quantitative Metrics 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:
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.
The Halstead Complexity Measures assess the computational complexity of the code. This metric leverages indicators like the number of operators and operands to measure the code complexity in terms of vocabulary, program length, errors, difficulty, efforts, size, testing time, and number of bugs in a module. The more complexity, the lower the code quality.
Cyclomatic complexity metric measures the structural complexity of the code. It is the measure of the number of linearly independent paths through a source code. If the cyclomatic complexity is greater than 10 then the code contains defects and needs to be rectified. This metric empowers developers to how difficult it will be to test, maintain, and debug their code. Combining this metric with size metrics such as liens of code enables you to assess the ease with which the code can be modified and maintained.
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. This is what Opsera’s Unified Insights Platform is built for. What used to take months can be done in minutes with our platform. The platform displays comprehensive software delivery analytics across your CI/CD process in a unified view — including Code Quality Metrics and other KPIs like Lead Time, Change Failure Rate, Deployment Frequency, and Time to Restore.
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 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.
Opsera’s Unified Insights tool is a powerful DevOps metrics dashboard that enables you to aggregate all the code quality metrics into a single, unified view. It helps gain end-to-end visibility into the metrics in true CI/CD categories, including Pipelines, Planning, SecOps, Quality, and Operations. Moreover, this persona-based dashboard provides code quality analytics targeting vertical roles, including developers, managers, and executives, to empower you to understand your DevOps processes from both practitioner and managerial perspectives and take better technical and business decisions. The platform provides fully aggregated and contextualized logs and reports, that help you to easily analyze the metrics and identify the issues in the code faster. The actionable intelligence empowers you to troubleshoot the code issues within no time. After all, Unified Insights Tool is built for improving code quality and efficiency.
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