Code quality is foundational to software efficacy, and ultimately the health of products and services. Every line of should add value, while being careful to reduce opportunity for risk.
This article explains what code quality is and how you can measure it. We'll look specifically at metrics that can indicate code quality, but will also consider the broader development environment and incentives that arise from all measurement.
What is code quality?
Code quality refers to the measure of how well-written and well-structured a piece of software code is. It encompasses various aspects such as readability, maintainability, reliability, efficiency, and adherence to coding standards and best practices. Essentially, code quality reflects how easily a codebase can be understood, modified, and maintained by developers, as well as its ability to perform reliably and efficiently under different conditions.
This does not exist in a vacuum. Code quality is dependent on various factors such as business goals, legibility and security. Good quality code is most helpful to software engineers, but improves user experience too as they are more likely to find your application reliable and secure.
Code quality draws from a few properties:
Readability
This is somewhat self-explanatory, referring to how well-structured and understandable code is to developers. It is measured by examining factors such as naming conventions, documentation quality, and the complexity of constructs used. Good code readability facilitates easier maintenance, debugging, and refactoring, keeping variable names meaningful and functions small and efficient.
Maintainability
Maintainability measures the ease with which software can be modified to fix defects, improve performance, or adapt to a changing environment. It's assessed through code complexity analyses, adherence to coding standards, and the modularity of the software architecture. High maintainability is crucial for extending the lifespan of software and reducing long-term costs associated with updates and enhancements.
Performance
Performance in the context of software refers to how efficiently a software system responds to user actions, utilizes resources, and processes data. It is measured by metrics such as response time, throughput, and should consider efficiency and resource utilization as well as raw speed. Optimizing performance ensures that software applications can handle expected workloads smoothly and provide a satisfactory user experience.
Reliability
Reliability measures the ability of software to perform its required functions under stated conditions for a specified period of time without failure. This attribute is quantified by tracking software reliability metrics, such as the frequency and severity of failures, mean time between failures (MTBF), and recovery time after a failure. High reliability is critical for maintaining user trust and minimizing disruptions in software-dependent activities, especially in critical systems where downtime can have significant consequences.
What are the characteristics of good code?
Developers often cannot give a succinct definition of what represents bad code. But like the Supreme Court assessing pornography, they know it when they see it. It’s typically unreadable or confusing, prone to breaking and lacks proper documentation. It is poorly organized and devoid of a logical structure, leading to the term “spaghetti code”. Reading it is like being in a maze without a map, and even small changes can cause it to break.
Good code is defined by clarity and function. It follows a consistent style, with structures and patterns that guide the developer through the code’s logic. When programmers write clean code with proper indentation and formatting it speeds up the development process and ultimately the time-to-market. It enables scrum and agile methodologies by being adaptable in changing environments.
Comprehensive documentation is part of good code, which enables maintenance and further development. High-quality code is also designed to be testable, meaning it supports validation of its functionality through testing. Reading good code is like strolling through a manicured garden, where plants are thoughtfully placed, paths are well-tended and the gardener has recorded maintenance details for future generations.
Why does code quality matter?
Code quality influences not just the development process, but the security, performance and overall user experience of the final product. Software quality, developer experience and user experience are all downstream of code quality, and getting it right makes for smoother interactions with faster response times for users. For developers, higher-quality code makes it easier to update legacy code, integrate new features and conduct code reviews. Solving for quality across every piece of code makes the development process more efficient while ensuring the longevity and success of the software project as a whole.
Today’s demands for quicker development cycles powered by continuous integration and DevOps principles make coding standards even more critical. Code quality is the foundation for software, and if you optimize every other part of the building process for speed and efficiency you’ll need to make sure the foundations are rock solid.
Benefits to code quality include:
Enhanced Reliability and Maintainability: High-quality code is robust and less prone to failures, making software systems more reliable. It also simplifies maintenance tasks, as the code is easier to understand and modify without introducing new errors.
Software Stability: Quality code practices ensure that software behaves consistently under a range of conditions, reducing the likelihood of crashes or unexpected behavior.
Faster Time to Market: By reducing the amount of time spent on debugging and reworking problematic code, projects can progress more rapidly from development to deployment.
Cost Savings: Investing in code quality from the outset can significantly reduce long-term costs associated with software development. High-quality, maintainable code requires fewer resources to fix and update, leading to substantial savings over the software's lifecycle.
Scalability and Adaptability: Quality code is designed with future growth in mind, making it easier to scale and adapt as requirements evolve. This ensures that the software can accommodate increasing loads and changing needs without extensive rewrites.
Compliance and Security: Adhering to high code quality standards helps in meeting regulatory compliance requirements and enhancing software security. Well-structured, clean code is less vulnerable to security breaches and more likely to align with industry regulations.
How can teams review code quality?
It’s crucial that code quality is checked and emphasized throughout the software development life cycle (SDLC). Using regular code reviews and code analysis tools at every step enables early detection, saving on costs and enabling a culture of continuous improvement.
Methods to review your code include:
Automated Quality Checks
Definition
Automated quality checks involve the use of software tools to automatically scan and analyze code for potential issues, such as syntax errors, security vulnerabilities, and code standard violations. These tools are integrated into the development process to ensure continuous assessment of code quality.
Pros/cons
Pros include the ability to quickly scan large volumes of code, identify known issues with precision, and enforce coding standards consistently. However, automation may miss complex bugs that require human insight to detect and can generate false positives that need manual review.
How it assesses different aspects of code quality
Automated quality checks assess code quality by evaluating code against a predefined set of rules or standards. They can measure various aspects such as code complexity, adherence to coding conventions, potential security issues, and other factors that can affect maintainability, performance, and reliability.
Manual Code Reviews
Definition
Manual code reviews are a systematic examination of source code by developers other than the original author with the goal of finding bugs, ensuring adherence to coding standards, and improving code quality. More insights on best practices for code reviews can be found in this article.
Pros/cons
The primary advantage of manual code reviews is the ability to catch complex code issues that automated tools might miss, including logical errors or issues with code clarity. Reviews also facilitate knowledge sharing among team members. The downside is that they can be time-consuming and depend heavily on the reviewers' expertise and biases.
How it assesses different aspects of code quality
Manual code reviews assess code quality by leveraging the insights and experience of human reviewers. They not only look for errors and potential improvements but also evaluate the readability, maintainability, and overall design of the code, offering a more nuanced understanding of its quality.
Static Analysis
Definition
Static analysis involves examining the code without executing it to find issues. It's a form of automated quality check known as static code analysis, focusing on identifying potential errors and vulnerabilities. For more details, refer to this article on static analysis tools.
Pros/cons
Static analysis can efficiently identify certain types of errors, such as syntax errors, type mismatches, and known security vulnerabilities, across the entire codebase. However, it might not catch runtime errors or complex issues that manifest only under specific conditions. Additionally, like other automated methods, it can produce false positives.
How it assesses different aspects of code quality
Static analysis tools evaluate code quality by scanning the source code for patterns that are known to lead to errors, such as variables that have accidentally been reused or functions that are too long. They can also check for compliance with coding standards, analyze code complexity, and identify security vulnerabilities, thereby covering a wide range of quality aspects including readability, stability, and security. Storing results from static analyses can boost your documentation efforts. Most programming languages have their own static analysis tools.
Ensuring high quality code is not about finding the single best approach to tests and reviews. It means using multiple methodologies, metrics and assessments to measure code quality in a comprehensive way. In the next section we consider some metrics that can help.
Eight metrics to quantify code quality
Cyclomatic Complexity
Explanation: This form of static analysis measures the complexity of a program by quantifying the number of linearly independent paths through it. Essentially, it is a prominent complexity metric to indicate how complex the control flow of the program is.
Example: Calculated based on the graph theory of a program's control flow graph, a lower number suggests simpler code with fewer potential bugs, while a higher number indicates greater complexity and potentially more test cases required.
Maintainability Index
Explanation: This index assesses how easy it is to maintain the code on a scale of 0-100. It takes into account factors like extensibility, number of lines of code, cyclomatic complexity, and Halstead complexity measures.
Example: A higher value (typically above 85) indicates better maintainability. It's calculated using a formula that combines several metrics, including volume of code, complexity, and comments. This helps identify areas where refactoring might reduce complexity.
Code Duplication
Explanation: Measures the amount of duplicated code within a codebase. High levels of duplication can make maintenance harder and leads to more bugs as there is an opportunity for divergent implementation of the same functionality.
Example: Detected using tools that scan for identical or similar code blocks. Reducing duplication can significantly improve code quality by ensuring consistency and reducing potential points of failure.
Unit Test Pass Rate
Explanation: This metric indicates the percentage of unit tests that pass during a test run. It reflects the reliability of the code under test conditions.
Example: A pass rate close to 100% is ideal. Lower rates may indicate problems in code logic or test coverage gaps, prompting further investigation.
Code Churn
Explanation: Code churn measures the frequency at which code is added, modified, or deleted within a repository. High churn rates can indicate instability or indecision within the development team.
Example: Tracking code churn over time helps identify areas of the codebase that are frequently changing and may benefit from additional design reconsideration or testing.
Average Code Review Time
Explanation: This is a quantitative measurement of the average time taken for code reviews. Long review times can indicate complexity or lack of clarity, whereas very short times might suggest insufficient rigor.
Example: Monitoring this metric can help teams understand and improve their review processes, aiming for thorough yet efficient reviews to maintain pace without sacrificing quality.
Technical Debt
Explanation: This quantifies the cost of choosing an easier or quicker solution in the short term instead of using a better approach that would take longer. High technical debt indicates that significant work is needed to improve the codebase.
Example: There are various methods to calculate technical debt, but one common approach is to quantify the effort required to fix all maintainability issues. Improving these areas can reduce the long-term costs of a project. For more ways to measure technical debt, you can refer to this article.
Dependency Graph Complexity
Explanation: This metric assesses the complexity of the dependency graph: the map of connections and relationships between components or modules in a codebase. A highly complex graph may indicate a tightly coupled system, which can make changes difficult and error-prone.
Example: By analyzing the dependency graph, developers can identify overly complex interdependencies and consider refactoring to a more modular architecture, simplifying the structure and potentially improving maintainability and testability.
Tips for improving code quality
Defining and measuring code quality is not an easy task, but even when you crack it your work is not done. Metrics reveal code quality, but they don’t improve it. Only by implementing changes through continuous improvement can you drive the results that improve code quality, and ultimately help stakeholders through faster time to market, improved developer experience and greater cost savings.
If this process is handled poorly it can actually have a negative effect. Increasing pressure on your team to deliver higher standards, without addressing their working environment and tooling, can lead to stress and burnout among your developers. Cynical employees may game the system, optimizing metrics at the expense of productivity and culture. There is a risk that your policies amount to “the beatings will continue until morale (code quality) improves”.
This risk is especially pronounced when you are not providing the right self-service options for developers. If they are unable to easily access the correct tools, documentation and support to improve their code then efforts to improve quality can make things worse. For a detailed explanation of this risk in the modern environment see this blog by Cortex’s Head of Developer Relations.
Provided you are getting your developer experience right, here are some tips to increase your code quality.
Follow coding standards
This is the most straightforward starting point. Adhering to coding standards ensures consistency, readability, and maintainability across the codebase. It simplifies the understanding of new and existing code for all developers, making it easier to identify errors, share code among team members, and onboard new developers.
Write meaningful documentation
Good documentation acts as a roadmap for your code, explaining not just what the code does, but how and why certain decisions were made. This is crucial for maintenance, troubleshooting, and future development, as it allows developers to quickly understand and work with the code without having to examine it line by line.
Utilize version control
Version control systems, such as Git, track and manage changes to the codebase, enabling multiple developers to work simultaneously without conflict. They also facilitate rollbacks to previous states and help in managing different versions of the code, making the development process more efficient and less prone to errors.
Perform code reviews
Code reviews are a critical step in the software development process, allowing for shared knowledge among team members and early detection of potential issues. They encourage collaboration, improve code quality by catching bugs and logical errors early, and ensure adherence to coding standards and best practices.
Automate testing
Automated tests verify the functionality of code quickly and efficiently, saving time and reducing the likelihood of human error. They allow developers to be more confident when make changes, and are best when combined with qualitative manual reviews.
Test throughout the SDLC
Integrating testing at every stage of the Software Development Life Cycle (SDLC) ensures issues are identified and addressed early, reducing the cost and effort required to fix them later. Continuous testing helps to maintain high-quality standards throughout the development process.
Set-up dashboards to increase visibility and collaboration
Implementing dashboards through internal developer portals can significantly enhance visibility and collaboration within development teams. The perfect solution should simplify the gathering and interpretation of data by the full range of developer tools in use, without overwhelming the team or requiring extensive administration after integration. These platforms help in tracking progress, identifying bottlenecks, and sharing insights across the team, fostering a culture of transparency and continuous improvement.
Track and improve code with Cortex
The Cortex IDP allows you define and track code quality metrics, but most importantly it embeds these in a system built for continuous improvement. Rather than setting orphaned metrics isolated from context, Cortex enables the transparency, developer collaboration and actionable insights needed to ensure your quality code is built in a setting that prioritizes developer experience and a culture of excellence. By acting as a single source of truth across different stakeholders and use cases, it provides the context needed to ensure that your efforts to improve code quality are working as planned.
Ensuring higher quality code means getting access to Cortex's extensive library of integrations and plugin framework which enables you to centralize and score against the outputs of your code quality solutions.
Some specific Cortex features that can optimize your code quality include:
Eng Intelligence: This feature provides deep insights into engineering operations, highlighting trends and patterns in deployment frequency and other critical metrics. It helps your teams make data-driven decisions regarding code quality in the context of broader engineering initiatives.
Scorecards: Scorecards offer a comprehensive overview of service health and team performance across workflows. By pairing code quality metrics with broader indicators of overall quality, Scorecards give you an oversight that helps you to investigate the root cause of any drops in standards.
Developer Homepage: This personalized dashboard acts as a hub for developers, offering quick access to relevant metrics, reports, and tools. It decentralizes some of the responsibility for code quality standards, and ensures that relevant data and information is always at your developers' fingertips.
Cortex transforms the way development teams approach code quality and performance, by automating monitoring and goal tracking across each. If you're keen on elevating your team's code quality as well as collaboration, reliability, and productivity, consider booking a demo today.
Additionally, for more insights into building a culture of reliability within your engineering team, watch this webinar on-demand.
