There are many ways to implement any given functionality, but some approaches result in code that is easier to maintain over time than others.

Poor code maintainability leads to:

• longer development cycles for new features,

• reduced estimation accuracy,

• higher risk of introducing defects during changes,

• and growing friction within development teams.

Maintainability is closely tied to developer experience (DX) and delivery predictability. Clear code, efficient tooling, and disciplined processes support maintainability, while poor DX often leads to shortcuts, brittle systems, and rising long-term costs.

Code Design, Code Quality, and the Bigger Picture

Code design and code quality are key factors of overall maintainability.

Compared to a book, you can think of code design as the story, and code quality as the writing (use of language). It’s easier to fix quality. Fixing design is often much harder.

From now on, I will refer to code design, quality, and maintainability as just codebase quality.

What Affects Codebase Quality?

Poor codebase quality rarely has a single cause. It is usually the result of multiple overlapping factors, such as:

• developer inexperience,

• time pressure,

• inefficient project management (frequent changes in scope or priorities after development has started).

Additionally, codebases naturally degrade over time:

• libraries and frameworks evolve,

• best practices change,

• teams rotate,

• and shortcuts taken in the past accumulate.

The best way to think about it is to treat this as technical debt - the cost of decisions postponed for “later” that eventually must be paid back, often with interest.

How Do We Maintain High Codebase Quality?

We use a combination of technical practices and process-oriented approaches, both in new projects and when joining existing codebases.

Code Review

One of the most important practices in managing codebase quality is having someone else review the changes. Another programmer can look at the code from the outside perspective and quickly assess if the code “passes muster”. They can also suggest better solutions. You can think of this as a form of asynchronous pair programming.

There are multiple ways to solve any problem in programming, so having this diversity of ideas is always beneficial, even for experienced programmers. Having another person review a programmer's work helps spot mistakes, point out things that should have more test coverage, or suggest a better, more concise syntax.

Pair Programming

Two heads are better than one, and for particularly complex problems, it sometimes makes sense to have two developers working on the same thing simultaneously. You can think of it as a continuous code review. It obviously requires more developer time, but usually results in much better solutions.

Static Analysis and Linting

Several tools help us automatically deal with the most common and easy-to-fix problems. These tools scan the code without actually running it and provide hints on formatting, performance, and even security.

While specific tools vary depending on the technology stack, the goal remains the same: detect quality issues early and enforce consistent standards across the codebase.

In Ruby-based projects, RuboCop with plugins remains the de facto standard for linting and static analysis. There are RuboCop plugins for many popular libraries, as well as rubocop-performance. It’s highly configurable and can help tailor the codebase to the preferences of developers. It can also autocorrect many common mistakes (like formatting). Used as a part of a CI build, it can work alongside a test suite to ensure quality and security.

In multi-language and frontend-heavy codebases, linters and formatters such as ESLint and Prettier play a crucial role in maintaining consistency and readability. By enforcing shared coding standards automatically, they reduce friction during code reviews and allow teams to focus on logic and architecture rather than formatting details.

Application Monitoring and CI/CD

We have experience with many tools for application monitoring, code metrics, and CI/CD. Our go-to for new projects is:

• Sentry (error monitoring),

• NewRelic (metrics, error monitoring, etc.),

• CircleCI (CI/CD),

• AppSignal (monitoring),

• Datadog (observability).

Depending on the project’s scale, architecture, and operational requirements. We regularly work with different observability and delivery platforms and adapt to tools already present in existing environments. But we are prepared to work with other tools that are present in existing projects. If they are missing, we will recommend the best solutions for a given codebase.

So why is this important? Error monitoring helps us catch problems much faster and have insights into why they happen. It’s always better to know what’s going on and not have to learn about issues from customers.

Metrics are important for optimizing applications – identifying poor performance hotspots and having charts are very helpful in determining which actions we should focus on.

Continuous Integration and Continuous Delivery (CI/CD)

A well-designed CI/CD process is a critical component of maintaining high codebase quality. Automated pipelines ensure that every change is validated through tests, static analysis, and security checks before it reaches production.

By embedding quality controls directly into the delivery process, teams reduce the risk of regressions, shorten feedback loops, and maintain consistent standards regardless of team size or project age. In legacy environments, introducing CI/CD is often one of the most impactful first steps towards improving reliability, predictability, and system stability.

Increase Test Coverage

Automated tests are probably the most underestimated part of an application. They usually get cut first when there’s time pressure, and this part of the technical debt is rarely paid off on time. The benefits of good test coverage are ignored because they’re invisible.

They include time not spent on manual testing and fewer cycles of code review and QA, because they catch many issues before the developer submits their code. They are also very important when updating dependencies of the application, like the Rails framework itself. They catch deprecations and incompatibilities.

All these things go underrated because they are a long-term investment, but in more extreme cases, their lack can lead to severe problems.

All these tools have been helping developers create and maintain better code, as well as learn new things. That last one is particularly underestimated, but in IT, we have to learn new things constantly as technology evolves. Using static analysis tools can help a lot with adapting new best practices and discovering new methods of solving old problems.

The Role of Framework Principles and Conventions

Convention Over Configuration

One of the core principles of Rails is convention over configuration. To new developers, it means ease of learning, but it really shines in bigger codebases because it helps reduce the volume of code. The sheer amount of files and lines of code in larger applications can be daunting, so anything that helps reduce these numbers is a good thing.

Class and Method Naming

Rails is famous for its generators that help make sure new parts of the application are named correctly and put in the right place. This, by itself, sets a convention and helps developers with structuring their code and naming methods and variables. Naming is immensely important for reducing conceptual overhead and letting the developer understand code faster and easier.

A Word on Documentation

An important part of maintenance, especially for long-running projects, is documentation. There are various tools to help organize it, like Jira’s Confluence, but in many cases, just a well-kept README file and code comments are enough.

Despite this, documentation tends to be one of those things that gets thrown out first when time or money constraints show up. And like any other element of code quality, its benefits are seen mostly in the long run.

The topic of how to write documentation and what on is a separate question that is out of the scope of this article. Just know that responsible developers (and management) shouldn’t forget about it, unless you want the new team to spend a month just figuring out a critical feature that needs to be updated as soon as possible, a couple of years on.

Improving Codebase Quality in Existing and Legacy Projects

We have vast experience joining existing projects to help teams regain control over codebase quality, delivery speed, and long-term maintainability. This work often involves legacy systems that have evolved over the years without consistent quality standards or clear ownership.

In such environments, low maintainability does more than slow development. It increases operational risk, makes security issues harder to detect, and significantly raises the cost of modernization.

Addressing these challenges requires a pragmatic, incremental approach: improving code quality, strengthening testing and monitoring, and reducing technical debt step by step without disrupting ongoing operations.

Incremental Improvements

When technical debt isn’t dealt with in due time, it grows, and resolving it after a long period of neglect (or just lack of development) requires a meticulous approach, because certain issues depend on each other, so we need to divide the work into tickets and resolve them in an efficient order.

Summary

Code maintainability has a direct impact on development speed, delivery predictability, and long-term system stability.

When codebase quality deteriorates, teams slow down, technical debt grows, and even small changes become risky.

Practices such as code reviews, automated testing, static analysis, and monitoring help teams maintain control over both new and legacy systems. Treated as a continuous responsibility, maintainability becomes the foundation for safer modernization, smoother scaling, and better developer experience.

In many organizations, improving codebase quality is also the first step toward broader initiatives such as a structured code audit or a comprehensive quality audit.