Optimizing DevEx: Reducing Cognitive Load in Complex Codebases

At Acid Tango, we talk a lot about developer experience, and not as a trendy term, but as something that deeply shapes how we work, how fast we ship, and how much we enjoy (or suffer through) a project. Good DevEx isn’t about flashy tools or perfect setups. It’s about creating an environment where developers can focus, understand the codebase quickly, and build without constantly second-guessing themselves.

One of the main blockers we’ve seen to a smooth developer experience is cognitive load - the mental effort required to understand and work with a system (we will talk about it). In software development, cognitive load builds up when we’re juggling too many abstractions, navigating unclear code structures, or trying to decode undocumented decisions. But it can also spike when there are too few abstractions – when everything is low-level, repetitive, or lacks structure. In both cases, the result is the same: onboarding slows, bugs creep in, collaboration gets harder, and motivation drops.

That’s why reducing cognitive load in complex codebases is so important. It’s not just about writing “clean code” - it’s about designing systems that are easier to reason about, extending features without anxiety, and helping teams move faster and with more confidence. In this article, we’ll talk the types of cognitive load, why they matter, and share practical ways we’ve learned to reduce them in real-world projects.

Understanding cognitive load in software development: what is it?

When we talk about cognitive load in development, we’re essentially talking about how much of our brain’s “working memory” is being used while writing, reading, or modifying code. Think of it like RAM for your brain: the more it's loaded up with irrelevant or overly complex stuff, the less room you have to focus on actually solving problems.

In software development, this becomes a critical factor. High cognitive load means slower progress, more context-switching, more bugs, and ultimately, more frustration. It’s one of the silent killers of productivity, and one of the main sources of burnout when working with large, messy, or unclear codebases.

Cognitive load theory (Sweller, 1988) breaks this mental effort into three types:

1️⃣ Intrinsic Load – this is the inherent difficulty of the task itself. For us, this could be designing an algorithm, understanding a domain model, or refactoring a complex system. It’s the unavoidable complexity, the stuff that actually needs thinking power.

2️⃣ Germane Load – this is the good kind of cognitive load. It’s the effort we spend learning, connecting the dots, and building mental models that help us solve the problem. Ideally, this is where we want most of our mental energy to go.

3️⃣ Extraneous Load – this is the mental overhead that shouldn’t be there. Poorly named variables, nested conditionals, long methods, inconsistent project structures, tech debt, and even interruptions or confusing documentation, all of these add to the noise and make development harder than it needs to be.

The goal, especially when working in complex codebases, is to minimize extraneous load, simplify intrinsic load when possible, and maximize germane load.

That means writing code that’s easier to understand, reducing context-switching, creating predictable structures, and removing any friction that gets in the way of focusing on the task at hand.

Simple practices like clear naming, modular architecture, encapsulating business logic, and limiting the scope of each function or class go a long way. Even the way we document - keeping it concise and close to the code - plays a role in lowering the mental cost of understanding how things work.

By being intentional about reducing this load, we don’t just make us faster — we make us happier, more focused, and more likely to stick around. Because at the end of the day, great DevEx isn’t just about tools or frameworks: it’s about creating space for developers to think clearly and do their best work.

Why reducing cognitive load matters (to you and to your company)

When working in a complex codebase, every extra decision, every confusing line, every unclear structure adds weight to your mental stack. And the truth is, our brains only have so much working memory to work with at a given time. The more of that capacity we waste on figuring out how to do something instead of just doing it, the slower and more error-prone we become. Why does it matter?

✅ Faster onboarding
When a new developer joins a team, they’re already absorbing a ton: the product, the domain, the people, the processes. If the codebase is also an unstructured maze, you’re practically guaranteeing a painful onboarding experience. But a well-structured codebase acts as a map. New teammates can focus on learning what matters, instead of reverse-engineering decisions that were never written down.

✅ Higher productivity
Every developer has felt the drag of trying to implement a feature in code they don’t fully understand. Hours go into figuring out where things are, how they connect, and what might break. When cognitive load is low, that time shrinks dramatically. Developers can move confidently and consistently, with a clear understanding of where logic lives and how to extend it. Tools, naming conventions, and architecture all serve as guardrails instead of hurdles.

✅ Fewer bugs
Bugs often aren’t just the result of bad logic…they come from misunderstandings. When the intent behind code is unclear, or logic is scattered across too many places, even experienced developers can miss important connections. A clean, focused codebase reduces this risk. There’s less guesswork, and more clarity about what’s supposed to happen - which means fewer bugs and less time spent debugging.

✅ Better collaboration
Software development is a team sport. When everyone is working with the same mental model of the codebase, things just flow better. Standardized patterns, modular design, and clear documentation help everyone stay aligned, even as the team grows. You can review PRs faster, hand off tasks more smoothly, and avoid those "wait, where does this actually live?" conversations that kill momentum.

So, let’s be real (for real): startups and fast-moving teams can’t afford engineers stuck at half-speed, constantly bogged down by confusing systems. Reducing cognitive load is one of the most effective ways to unlock consistent performance, avoid burnout, and build products with fewer detours.

How to reduce cognitive load in complex codebases (you can thank us later)

Here are the practices that, in our experience at Acid Tango, have the biggest impact on reducing cognitive load - especially when working on real products, under real deadlines.

🔸 Have a clear way of working and a well-structured codebase

One of the most overlooked causes of cognitive load is ambiguity. If I’m writing a new feature and I’m unsure where to put the code, how to name things, or what the conventions are, my brain is already working overtime before I even write a line.

That’s why we always define a shared way of working before diving into code.

Project structure: we lean toward feature-based organization. This keeps everything related to a given feature (UI, logic, tests, etc.) together. It reduces mental context-switching - I don’t need to look in five folders to understand how “invoices” work.
Naming conventions: clear, consistent naming is a huge win. It sounds basic, but when variable names reflect intent (“userHasPermissions” vs. “checkAuth”), the code becomes self-explanatory. No extra documentation needed.
Testing mindset: we treat tests as part of the design, not an afterthought. Unit tests go alongside logic, integration tests mimic real scenarios, and we use naming patterns that help you quickly understand what is being tested and why.
Vertical slicing: this means building features end-to-end — from backend to frontend — in a small, shippable way. It helps keep each slice focused and reduces the need to hold the entire system in your head at once.
Docs — minimal but useful: over-documenting is just as harmful as no documentation. We prefer README.md files placed next to complex modules, focused on why something exists, not what every function does. The code should explain itself as much as possible.

One quick example: on one of our recent projects, we inherited a legacy backend where logic was split between a services/, helpers/, and utils/ folder… with no clear boundary between any of them. Adding a new feature required checking multiple files to make sure logic wasn’t duplicated. Once we restructured things around feature folders and use cases (more on that below), our onboarding time for new devs dropped a lot — from days to hours in some cases.

🔸 Avoid bloated services that make a lot of stuff

A common source of overload in codebases is the “mega service” - a class or module that tries to do everything. Fetch data, apply business logic, trigger events, manage state... it’s all in there. And sure, it might work at first. But fast-forward a few sprints, and no one wants to touch it.

Instead, we model our logic around use cases - single-responsibility units that reflect something meaningful the system needs to do. Think CreateInvoice, SendWelcomeEmail, CalculateMonthlyFee. Each use case has:

A clear input and output
A focused responsibility
Few dependencies (injected when needed)
Its own test suite

This makes them easy to understand in isolation. You can read one use case and understand what it does without diving into three other files. Plus, it aligns your code with your business language - which is gold for onboarding, debugging, and collaborating with non-dev stakeholders.

Also: because each use case is a standalone unit, tests are faster to write, and way less fragile. You don’t need to mock the whole universe to test a single rule.

We like to think of this as designing the code to tell a story. Instead of reading vague service functions like processInvoiceData, you see high-level logic composed of verbs and intent: validateClient(), generatePdf(), sendToBilling(). That structure lowers mental overhead dramatically.

🔸 Try to avoid code duplication avoiding early generalisation

This one’s tricky, because developers are trained to spot repetition and eliminate it. But sometimes, eliminating repetition too early leads to abstraction that’s harder to reason about than the original duplication.

Let’s say you’ve got three pieces of logic that look similar - maybe a few if conditions with shared structure. You extract a generic helper function, add some flags to handle the differences, and boom - now the code is more compact... but also more confusing.

Our approach? Let patterns emerge. Duplication is okay at first. If a chunk of logic repeats and stays stable over time, then it’s worth extracting. But make sure the abstraction reflects a clear domain concept, not just superficial similarity.

On the flip side, when we do spot logic scattered across the codebase — like the same “check if user is admin and has active subscription” conditional — we extract it into a meaningful class or function. Something like UserPermissions.canManageAccount(user) is much easier to read and reason about.

Think of encapsulation as compression: done right, it reduces volume without losing meaning. Done wrong, it becomes a black box that increases mental effort instead of reducing it.

🔸 Just redesign times

Codebases don’t magically become cleaner. They get better because someone made them better — bit by bit, sprint after sprint.

We are always encouraged to follow something we call micro-refactoring: every time you touch a piece of code, leave it a little better than you found it. Rename something, extract a method, remove dead code, add a missing test. These aren’t big rewrites — they’re small, intentional nudges that keep the codebase healthy over time.

When we plan sprints, we also look for opportunities to dedicate time to intentional redesigns. That doesn’t mean pausing everything for a month to rebuild the backend — it means identifying pain points (slow tests, hard-to-change modules, unreliable endpoints) and scheduling small refactors or improvements alongside regular feature work.

We often work with clients who move fast — startups, innovation teams, early-stage products — so we don’t always have the luxury of stopping to “fix everything.” But what we can do is prioritize tech debt just like we prioritize features. If something is slowing down delivery every sprint, it’s worth investing time to improve it.

Example: in one project, our domain logic was deeply coupled to a third-party API. Every time their schema changed, we had to refactor tons of logic. So we took a sprint to extract a domain layer that wrapped the API and translated it into our own models. It took a few days — but the long-term reduction in cognitive load (and future bugs) was absolutely worth it.

Are you ready to reduce your cognitive load?

Reducing cognitive load means faster features, better collaboration, and fewer late-night debugging sessions. Most importantly, it means you actually enjoy the work you're doing.

This is what good DevEx is all about. It’s not about flashy tech or the “perfect” architecture - it’s about clarity, simplicity, and helping developers stay in flow. Every choice you make in a codebase either adds weight or removes it. Choose the ones that lighten the load.