Code of the highest caliber is dependable, efficient, bug-free, and matches user needs. It can handle mistakes or peculiar circumstances. It is also simple to use, update, and add new features to. It can also run on as many machines as is reasonably possible due to its mobility.
The codebases that development teams work with are dynamic. They change, add, and remove existing code to bring new functionality or increase speed. Because they read a lot of code, readability is synonymous with quality in their eyes. Regretfully, its quality tends to deteriorate over time due to frequent modifications to the code. Examining the elements that contribute to legible, intelligible, and sustainably better code is made possible by this challenge.
What Is Code Quality, And Why Should You Care?
Understanding what is code quality metrics? is crucial, as code that is easier to read also runs quicker and with fewer mistakes, facilitating better comprehension and modification. This enhanced readability leads to improved extensibility and maintainability, ultimately resulting in fewer bugs and faster development, thereby reducing costs over time. These qualities characterize good code, but how can development teams improve readability?
Using descriptive variable names is a good place to start, but appropriately discarding unmanaged resources also helps. Regardless of seniority, the latter is simple to ignore, whereas the former is straightforward to apply at all skill levels. Although it is a basic practice, giving descriptive variable names considerably improves readability. To dispose of resources successfully, one must be aware of the instruments at their disposal and exercise caution when handling them. Metrics used to assess code quality can be classified as either quantitative or qualitative.
Quantitative Code Quality
Numerical values can be used to define quantitative quality measures. For instance, the percentage of code that executes during unit tests, or code coverage, is a discrete number that ranges from 0% to 100%. The probability of undetected flaws decreases with increasing value. Code quality is probably higher.
Take into account the typical number of lines of code (LOC) for each class or function. Fewer lines make a text easier to read overall. It is improbable that the 1,000+ LOC functions that the majority of seasoned programmers have faced were simple to read or comprehend. Another measure of maintainability that is more intricate is known as cyclomatic complexity. For example, Visual Studio (by default) supports its measurement, even if we don't often use it.
Source code's cyclomatic complexity determines how many routes are present. If-then expressions, loops, switches, and other control statements increase the cyclomatic complexity of your code. It should come as no surprise that greater complexity increases maintenance difficulty and increases the chance of errors.
Quantitative measures are important, but they only tell half of the story. It is not even guaranteed that the code will be error-free with 100% test coverage. However, a high cyclomatic complexity does not always imply serious problems with maintainability.
Quantitative code metrics interpretation is a subjective process. While some team members believe that 50% code coverage is sufficient, others may believe that 80% is the minimal amount that is appropriate. Thankfully, quantitative measurements are balanced by their qualitative equivalents.
Qualitative Code Quality
Although qualitative metrics are difficult to quantify, they are just as important for evaluating codes. Think about how important it is to follow coding guidelines, give objects meaningful names, or set a maximum line width for a codebase (a line width is a number, but its impact on code quality is not). These procedures, when combined, can serve as the cornerstone of very good code.
Qualitative measures may be quite subjective. While some programmers feel more comfortable using abbreviated names like ord or cust, others prefer longer variable names that explain their purpose. Languages have different naming traditions. Because of this, defining qualitative measurements becomes more challenging, particularly for individuals whose first language may differ from that of their peers.
Regular code reviews are an essential approach to improving code quality and keeping the dialogue regarding subjective quality metrics open. Colleagues can evaluate each other's overall code quality through this approach.
A group of five developers, for instance, can individually contribute code that needs to be approved by two or more peers before it can be merged back into the main branch. This method can identify problems that may have gone overlooked. Additionally, developers can exchange insights and stay abreast of the latest software updates by engaging in knowledge-sharing sessions. This collaborative learning environment enhances awareness of current software code quality metrics and industry best practices.
Continuous Quality: Measuring Code Quality In A CI/CD Pipeline
Development teams may monitor and evaluate code quality with the use of numerous tools and guidelines. It's simple to forget to review code before submitting it to source control, regardless of the tools you're using. Integrating these tools into your continuous integration and deployment (CI/CD) pipelines is crucial for this reason. By doing this, you can make sure that every source control commit includes a run of your code quality checks.
A linter, for instance, can run on a build or before a commit and verify that your code compiles with the coding standards of your organization. In the landscape of CI/CD solutions, the role of code quality metrics is exemplified by tools, which seamlessly integrates with various quality assessment tools such as SonarQube, Pylint, and ESLint. The availability of numerous options further emphasizes the pivotal role that code quality metrics play in the continuous integration and deployment processes.
ESLint can verify that you've used the var, let, and const keywords correctly in addition to checking your JavaScript syntax. It also evaluates whether the scope of your variables is unintentional, which could result in unexpected behavior. For Python, Pylint carries out the same functions. Linters can be customized to fit your unique requirements and preferences.
A robust tool, SonarQube can search for thousands of bugs across multiple programming languages. In addition to probable infinite duplication, hard-coded credentials, often occurring typing errors, and SQL UPDATE and DELETE statements without WHERE clauses, it can also identify duplicate code.
Although there are several strong alternatives, such as Code Climate, SonarQube has emerged as the de facto standard for code inspection. You may learn about potential issues, best practices, errors, and even security risks with the help of an efficient code analyzer.
You can send out an email or Slack notice listing all faults and warnings, or you can make a build fail on specific issues using a linter or code analysis tool. Unit tests must be configured and executed as part of your CI/CD pipeline. If a unit test fails, your build will not succeed. In addition, if your code coverage is less than a predetermined level, your build may fail.
Conclusion
Encouraging strong software development requires high code quality. The quality of its code strongly impacts the readability, maintainability, and extensibility of a project. In addition to lowering technical debt, a clean, well-organized codebase also reduces the likelihood of errors, which facilitates more efficient development processes and more affordable solutions. Beyond the development stage, the advantages have a positive impact on the whole software lifecycle.
Ensuring code quality at every stage of development is a practical and very beneficial approach. Although it is best to start this focus early in the project, it is never too late to put code quality assessment and improvement strategies into action. Effective solutions include following coding standards, using automated testing tools, and doing routine code reviews. Implementing robust code quality assessment strategies ensures that the software remains adaptable to evolving requirements, enhancing the effectiveness and affordability of maintenance and future enhancements.
