Reducing cognitive load in software development

Most of my writing focuses on organizational design, systems thinking, and leadership practices because I firmly believe that's where we can create the most broad-scale, long-lasting change. Over the past year however, conversations with CEOs have revealed a consistent pattern: their engineering teams are drowning in technical debt, struggling to build momentum, and burning out trying to ship features that should take weeks but somehow take months.

These leaders know their organizational structures need work. They've heard about Team Topologies, Org Topologies, empowered teams, understand they need better product strategy, and hopefully read Leadership without Control! But they're facing an immediate problem: their engineering teams can't build maintainable software at a sustainable pace.

This got me thinking about taking a different approach. Instead of starting with large-scale organizational transformation, what if we focused on what engineering teams can do right now to reduce their cognitive load and ship more effectively? What practices can any team adopt, regardless of their organizational context, to make their work more sustainable?

Let me be clear about what this article covers and what it doesn't. This isn't about organizational dynamics, team boundaries, or building the right thing. This is entirely about making it easier to build things right. I absolutely believe those organizational and strategic elements are crucial—without them, even the best engineering practices can only take you so far. At best, we'd have happy engineering teams that ship consistently but have no clue whether they're shipping the most important things for the organization.

While organizational transformation is important, it's not an either/or situation. You might find it much less complex and daunting to start with these engineering practices rather than reimagining your entire organization. These skills create immediate improvements while building capability for larger changes.

You're in charge, so the buck stops with you. Leaders bear the largest responsibility for the effectiveness of their organizations. You set the rules of play. Your policies, approval processes, and cultural expectations either enable or prevent your teams from adopting these approaches. As you read this, ask yourself: how might you be contributing to your teams' cognitive load through policies that run counter to these practices?And before we get started, I apologize in advance for the length of this article but, I thought it important to give this topic the thorough treatment it deserves. Along the way, I’ll share some amazing resources I’ve found to be extremely valuable in my learning: articles and books that will help you (or at least your engineering teams) master these topics.

The cognitive load crisis in engineering teams

Cognitive load in software development has three types: intrinsic (the task complexity), extraneous (poor presentation), and germane (building mental models). We focus obsessively on intrinsic load—"our code is too complex for this change"—while ignoring how our practices multiply the other two.

The real problem is that our common practices force engineers to waste mental capacity navigating artificial complexity instead of solving actual problems. Working alone when you could collaborate. Managing branches when you could work from trunk. Waiting for manual tests when you could have instant feedback.

Software development is fundamentally a team sport. Too often we try to optimize for individual productivity: block hours of maker time, reduce "meetings" and collaboration opportunities, assign tickets to individuals . . . but the more we actually leverage the team as a unit, not only does the team become more effective, but more importantly, individual cognitive load gets reduced because it's diffused across the entire group. The team can then handle magnitudes more complexity than the sum of what the individuals could handle working separately.

Consider these widespread patterns that create unnecessary overhead:

Large, infrequent integrations where teams work in isolation for weeks, then spend days resolving merge conflicts and integration issues. And yes, coding features in their own branches that are open for longer than a day is an example of working in isolation. I've watched teams spend more time resolving conflicts than building features because they waited too long to integrate their work.

Fragmented ownership and knowledge silos where only specific people understand critical parts of the system. When Aarti goes on vacation, her team grinds to a halt on anything touching "her" components. I've seen entire releases delayed because one person held all the knowledge about a critical integration.

Manual testing and deployment processes that require engineers to remember dozens of steps, coordinate across multiple systems, and pray nothing breaks. There's also the issue of how long these processes take, how well documented the test scripts are, and whether everyone follows them exactly. I once worked with a team where deployment required a 47-step checklist that took three hours and still failed 30% of the time.

Context switching between multiple complex systems where engineers jump between different codebases, each with its own patterns, naming conventions, and mental models. Every switch requires reloading an entirely different set of assumptions and knowledge.

Long-lived branches and async code review processes where engineers maintain parallel mental models of how code should work while waiting days or weeks for feedback. By the time reviews come back, the original context has evaporated.

Too much work in progress where teams start multiple features simultaneously, creating inventory that explodes cognitive load. The more unfinished work exists, the more mental overhead required to track status, dependencies, and context across multiple efforts.

The predictable result is that engineers become afraid to make changes. When you're not confident about the impact of your modifications, when testing is manual and slow, when integration is painful and unpredictable, the rational response is to avoid changing anything unless absolutely necessary.

This fear drives compensating behaviors that make the problem worse. Teams add more approval processes, create elaborate documentation requirements, and build complex staging environments. Each compensation mechanism adds more cognitive overhead while failing to address the root cause: the engineering practices themselves create an environment where change feels dangerous.

The under-appreciated value of maintainability

Here's what few people understand when it comes to building software: the speed of the first implementation doesn't matter. What matters is how quickly and safely you can modify, extend, and improve that code over months and years.

Yes, I understand the importance of shipping fast and early to validate ideas and seize opportunities but even in those situations, none of that matters if you can't respond to what you're learning. The ability to iterate rapidly based on feedback is what creates competitive advantage, not the speed of the initial implementation.

We talk about "writing code" as if the initial creation is the hard part. In reality, software development is primarily about maintainability. I've watched brilliant engineers spend entire afternoons figuring out how to make a two-line change because the codebase was impossible to navigate. That's not a problem with the engineers—that's a problem with the practices that created an unmaintainable system.

Kyle Griffin Aretae, whose posts I follow often, captures this perfectly with his ChEFS principle: every change should be Cheap, Easy, Fast, and Safe. This isn't just a nice goal—it's the fundamental requirement for sustainable software development. When changes are expensive, difficult, slow, or risky, teams naturally slow down. They become conservative. They accumulate technical debt. They spend more time working around problems than solving them.

This becomes even more critical now that AI can generate code at unprecedented speeds. Agents help you write faster, but they can't make bad code easier to understand or modify. These practices create a virtuous loop—clean, well-tested, standardized code provides better context for AI assistance. You get more value from your AI tools by following these practices alongside them.

Practices that reduce cognitive load

These practices aren't religious doctrine. They're tools for reducing the mental effort required to build and maintain software. While I'm not dogmatic about whether these must be followed and implemented by every team, I will definitely say that you will have a very difficult time being ChEFS without these. Sure, you can ship valuable products and potentially keep stability issues at bay without these practices, but it's going to be really damn hard to do so at pace with anything that's not trivial or that involves more than a couple of people.

I'm presenting them in order of implementation difficulty, with each practice building on the ones before it.

Automated safety nets through comprehensive testing

Before you can reduce cognitive load through other practices, you need confidence that changes won't break existing functionality. Automated testing provides that confidence.

Tests aren't just about catching bugs. They're about reducing the mental effort of making changes. When you have fast, reliable tests that run automatically, you don't need to hold the entire system's behavior in your head while making modifications. The tests tell you immediately if something breaks.

Speed matters more than coverage. You want tests that give you feedback in seconds. Anything longer leads to context switching. Of course, more complex systems require tests that cover the whole system and can't happen in seconds, but keep those to a minimum and run them only after all other fast feedback checks pass. The goal is to ensure that tests covering your critical behaviors run in seconds.

In one of my past teams, we had a rule: if the suite takes more than 3 minutes on your machine, pencils down and fix the suite. The moment feedback slowed down, engineers started multitasking while waiting for results. That context switch killed their effectiveness more than any technical debt ever could.

A side-effect of these fast tests is that you can easily understand when you break something and use that to "stop the production line" to get it back to working order. I'll cover more of this in the continuous integration section.

Focus on behavior tests over implementation tests. Tests should validate what the code does for users, not how it does it internally. Implementation tests break when you refactor, creating noise that masks real problems. Behavior tests remain stable while giving you freedom to improve the underlying code.

Comprehensive testing transforms code modification from a high-stakes, high-stress activity into routine work. Engineers stop being afraid of breaking things because they trust the tests to catch problems immediately.

Collective code ownership and standardization

Knowledge silos create massive cognitive overhead. When only specific people can work on specific parts of the system, every change requires coordination, waiting, and knowledge transfer. The team's effective capacity becomes limited by bottlenecks rather than total capability.

Collective ownership means anyone can modify any part of the codebase. This sounds chaotic, but it actually reduces cognitive load through several mechanisms:

Shared mental models develop when multiple people work on the same code. Instead of one person holding all the knowledge about a component, understanding becomes distributed across the team. This redundancy makes the team more resilient and reduces the burden on individuals.

At Split, we deliberately rotated who worked on different parts of the system. The first time someone touched an unfamiliar area, they might have needed help. The second time, they were contributing improvements. By the third time, they were helping onboard others. Knowledge spread naturally without formal training programs.

Consistent patterns emerge when everyone contributes to all parts of the system. Teams naturally converge on approaches that work well and abandon those that don't. This standardization reduces the cognitive switching cost when moving between different areas of the code.

Architectural guidelines help reduce decision fatigue. Instead of debating fundamental approaches for every feature, teams establish principles like "prefer simple pipelines over complex orchestration" or "use domain language in code naming." These guidelines eliminate overhead by making many decisions automatic.

Ubiquitous language connects code directly to business concepts. When the code uses the same terms customers use, engineers spend less mental energy translating between technical and business domains. A customer should be able to read feature code with minimal guidance—if they can't, the cognitive load for engineers will be high too. On this topic "Domain-Driven Design" provides the foundation for building this shared language that reduces translation overhead between business and technical domains.

Collective ownership requires psychological safety and trust. Teams need confidence that colleagues will maintain code quality and respect existing patterns. This trust develops through pairing, shared standards, and consistent practices.

Small, frequent changes through test-driven development

Large changes are cognitively expensive. When you're modifying hundreds of lines across multiple files, you need to hold complex mental models of how everything fits together. Small changes reduce this complexity dramatically.

Test-driven development enforces small changes through its cycle: write a failing test, make it pass with minimal code, refactor if needed. Each cycle handles one small piece of functionality, keeping the cognitive load manageable.

The key piece of TDD is that it makes it much easier to keep only a small bit of context in your head at any one point in time. The previous tests ensure you don't regress, and you're only focused on making this one test pass with the minimum amount of changes. That keeps the required cognitive load at bay.

I've watched engineers struggle for hours trying to implement large features all at once, holding dozens of edge cases and integration points in their head simultaneously. The same engineers, when they learned TDD, could build the same features in half the time because they only needed to think about one small piece at a time.

TDD works as a requirements discovery tool. Writing tests first forces you to think clearly about what the code should do before worrying about how it should work. This separation reduces cognitive load by handling design decisions sequentially rather than simultaneously.

The testing-first approach also builds confidence incrementally. Each passing test represents verified functionality that you don't need to worry about while working on the next piece. Your mental model grows gradually rather than trying to encompass everything at once.

Kent Beck's insight about TDD applies here: it's not really about testing. It's about having conversations with your code in small, manageable chunks. Each test is a hypothesis about behavior. Each implementation is an experiment. Each refactoring is an improvement based on what you've learned. His book, “Test-Driven Development: By Example" remains the definitive resource for learning and practicing TDD.

Pair and ensemble programming

Working alone forces you to hold everything in your head simultaneously. Not to mention that it also means you're only relying on your own knowledge, your own expertise, your own brain. Pairing and ensemble programming distribute cognitive load across multiple people, making complex problems more manageable.

Shared problem-solving means no single person needs to understand every detail. While one person focuses on implementation tactics, another can think about design strategy. While one person navigates the current code, another can consider edge cases or future implications.

Real-time knowledge sharing eliminates the overhead of knowledge transfer sessions, documentation updates, and code reviews. Knowledge flows naturally as people work together, building shared understanding without explicit effort.

At Split, some of our most complex algorithmic work happened through ensemble programming. Features that would have taken individual engineers weeks to understand and implement were completed in days because the team could leverage collective expertise in real-time.

Higher quality solutions the first time reduce the cost of rework. When multiple people review every line as it's written, you catch design problems, bugs, and improvements immediately. This eliminates the expensive cycle of implementation, review, debugging, and refactoring that characterizes solo development.

Natural WIP reduction happens automatically with pairing and ensemble work. Instead of multiple people working on separate features simultaneously, the team focuses on fewer things at once. This reduces context switching and interruptions. Fewer pull requests mean fewer review notifications, fewer context switches, and less cognitive fragmentation.

Reduced debugging cycles because problems get caught immediately rather than discovered later when context has been lost. Debugging is often more cognitively expensive than initial development because you need to reconstruct your mental model from incomplete information.

The collaboration overhead is real but temporary. Teams need time to develop effective pairing and ensemble skills. But once teams learn to work together smoothly, the cognitive load reduction is dramatic.

"Software Teaming: A Mob Programming, Whole-Team Approach" provides excellent guidance on building effective collaboration practices that distribute cognitive load rather than just adding coordination overhead.

Continuous integration and frequent commits

Integration complexity grows exponentially with time. Two people working separately for a day create manageable integration challenges. Two people working separately for a week create significant conflicts. Two people working separately for a month create integration nightmares.

Continuous integration reduces cognitive load by keeping integration complexity constant and low. When you integrate multiple times per day, conflicts are small and context is fresh. You can resolve issues quickly without rebuilding complex mental models of what changed.

Fast feedback becomes critical here. When integration breaks the build, you need to know immediately so you can fix the problem while context is still fresh. This is where those fast tests become vital—they let you "stop the production line" the moment something breaks, preventing broken code from propagating and forcing expensive context reconstruction later.

I've seen teams where broken builds stayed broken for days because the feedback was too slow to be actionable. By the time someone noticed the problem, the person who caused it was working on something completely different and had to spend hours remembering what they were trying to accomplish.

Shared responsibility for codebase health emerges when everyone integrates frequently. Instead of integration being a specialized skill owned by specific people, it becomes routine work that everyone handles. This distributes the cognitive load of maintaining system coherence.

Frequent commits force you to create coherent, incremental changes. Each commit should represent a complete thought—a small improvement that makes sense in isolation. This discipline reduces cognitive load by preventing the accumulation of complex, multi-faceted changes.

The practice requires discipline around commit size and frequency. Large, infrequent commits defeat the purpose by reintroducing integration complexity. The goal is making integration so routine that it requires minimal cognitive effort.

Decoupling deployment from release

One of the highest-stress aspects of software development is coordinating deployment timing with feature readiness. Traditional approaches, especially in enterprises with fixed release schedules, require perfect coordination: code must be complete, tested, and ready exactly when those predetermined windows open.

Decoupling deployment from release eliminates this coordination overhead through techniques like feature flags, branch by abstraction, or building inactive code paths that remain unreachable until explicitly activated.

Feature flags let you deploy code that's disabled in production. You can merge changes continuously without exposing incomplete functionality to users. This eliminates the stress of coordinating deployment timing with feature completion.

Branch by abstraction involves building new functionality alongside existing code, then switching over when ready. You can deploy the new code without activating it, reducing deployment risk and timing pressure.

Inactive code paths mean building features behind off switches that aren't connected to user-facing functionality until the final integration step. Users can't access incomplete features, but the code deploys safely with the rest of the system.

At Chase Digital and Chase UK, we deployed new banking features days and/or weeks before activating them for customers. This separation let engineers focus on building good solutions without the pressure of deployment coordination. Features went live when they were ready, not when the deployment calendar dictated.

The key benefit: engineers can focus on building good solutions without the stress of deployment coordination. Features get deployed incrementally as they're completed, rather than waiting for arbitrary release windows.

Paul Hammant’s "Trunk-Based Development and Branch By Abstraction" covers the full implementation details for making this approach work at scale.

Trunk-based development

Trunk-based development represents the ultimate reduction in version control cognitive load. Instead of managing multiple branches, feature branches, and merge strategies, everyone works from a single trunk with frequent integration.

This practice requires all the previous practices to be sustainable. You need comprehensive testing to ensure trunk stability. You need collective ownership so anyone can fix problems. You need small changes to minimize conflict probability. You need pairing to catch issues before they reach trunk. You need continuous integration discipline to keep integration complexity low. You need deployment/release decoupling to deploy safely without coordinating feature completion.

Eliminated branch management overhead removes an entire category of cognitive load. No more remembering which branch contains which changes. No more resolving complex merge conflicts. No more coordinating integration strategies across multiple parallel efforts.

Single source of truth means everyone works with the same codebase state. You don't need mental models of how different branches might interact or what the "real" current state of the system looks like.

Simplified workflow reduces process overhead. The development process becomes: make small change, test, commit, repeat. No branching strategies, no merge coordination, no complex integration ceremonies.

The practice feels risky if the supporting practices aren't in place. Without good tests, frequent commits to trunk create instability. Without small changes, conflicts become frequent and complex. Without collective ownership, problems don't get fixed quickly. But when the foundation is solid, trunk-based development provides maximum cognitive load reduction.

Continuous refactoring

Refactoring isn't optional maintenance—it's essential cognitive load management. Code that's hard to understand today will be harder to understand tomorrow unless you actively improve it.

Hands-down, Kent Beck’s "Tidy First?" needs to be on your shelf if you want to master this. The principle is simple: before making functional changes, first make the code easy to change. This might mean extracting methods, clarifying names, or restructuring for better readability. These preparatory changes reduce the cognitive load of the subsequent functional work.

Preventing cognitive debt accumulation through regular refactoring keeps the codebase maintainable over time. Instead of letting complexity build up until it becomes overwhelming, you address it incrementally as part of regular development.

I've worked with codebases where engineers needed three days just to understand how to make a simple change because nobody had invested in keeping the code readable. The same change in a well-maintained codebase took thirty minutes.

Making code readable for future maintainers includes your future self. Code that's obvious today might be mysterious in six months. Refactoring for clarity reduces the cognitive load of rediscovering your own thinking.

The economics of clean code compound over time. The effort invested in making code clean and clear pays dividends through every future interaction with that code. Conversely, technical debt grows exponentially—code that's hard to understand becomes harder to modify safely, leading to more problems and more complexity.

Continuous refactoring works best when it's integrated into regular development rather than treated as separate "cleanup" work. Each time you touch code, you have the opportunity to leave it slightly better than you found it. These small improvements accumulate into significant cognitive load reduction over time.

To round out your library, Martin Fowler’s "Refactoring: Improving the Design of Existing Code" provides the complete catalog of techniques for keeping code maintainable without breaking functionality.

Finishing more, starting less

One of the most overlooked sources of cognitive load is work in progress. The more unfinished work exists in the system, the more mental overhead required to track status, dependencies, and context across multiple efforts.

It may sound counterintuitive when you have more people, but it actually increases flow and throughput while reducing cognitive load when the team collectively is finishing more things, starting fewer things, and has fewer balls in the air.

Reduced WIP means fewer context switches, fewer status updates, fewer coordination meetings, and less mental juggling. When the team focuses on completing work rather than starting new work, cognitive load drops dramatically.

Finishing more creates momentum and psychological satisfaction while eliminating the overhead of maintaining partial work. Completed features don't require mental tracking—they're done.

Starting less forces prioritization conversations that eliminate low-value work before it consumes cognitive resources. Teams become better at saying no to work that isn't truly important.

At Split, we deliberately limited active development to three major initiatives for some time until we got better at constraining WIP through other methods. This constraint forced us to finish things before starting new ones. The result: features shipped faster and engineers felt less scattered.

The discipline of limiting WIP often reveals hidden bottlenecks and inefficiencies in the development process. Teams discover that their true constraint isn't the number of people working, but the number of things being worked on simultaneously.

The multiplier effect

These practices don't just add up—they multiply each other's benefits. Testing enables refactoring. Refactoring enables collective ownership. Collective ownership enables continuous integration. Continuous integration enables trunk-based development. Each practice makes the others more effective.

The sequence matters because some practices depend on others for sustainability. You can't do trunk-based development safely without good tests. You can't maintain collective ownership without standardization. You can't refactor confidently without comprehensive testing.

This interdependence explains why trying to skip steps usually fails. Teams that attempt trunk-based development without the supporting practices create chaos rather than cognitive load reduction. Teams that try to implement collective ownership without standardization create inconsistency rather than shared understanding.

Building team capability vs just individual productivity becomes the focus. These practices develop shared skills, shared understanding, and shared responsibility. The team becomes more capable as a unit, not just as a collection of individuals.

Creating sustainable development pace happens naturally when cognitive load decreases. Engineers can maintain focus for longer periods. They make fewer mistakes. They spend less time debugging and more time building. The pace feels sustainable because the work itself becomes less mentally taxing.

The investment in learning these practices pays dividends immediately and compounds over time. Teams that master these approaches consistently outperform those that rely on individual heroics or process complexity to manage software development challenges.

"Accelerate" provides the research foundation showing how these practices drive organizational performance through reduced cognitive load and increased development effectiveness.

Getting started

Most practices work independently and can be introduced gradually based on your team's biggest cognitive load pain points. However, let's be realistic—you can't just decide to implement trunk-based development without a solid foundation of the other practices.

Start with automated testing if you don't have fast, reliable feedback loops. Everything else becomes risky without this foundation. Focus on behavior tests that run in seconds and give you confidence that changes don't break existing functionality.

Add standardization and collective ownership once you have testing in place. Establish shared patterns and naming conventions. Start having multiple people work on the same parts of the codebase. Build redundant knowledge across team members.

Introduce small changes through TDD or similar practices. Break large features into smaller increments. Commit frequently. Build habits around incremental progress rather than big-bang implementations.

Experiment with pairing and ensemble programming for complex problems or knowledge sharing. Don't try to pair on everything immediately—start with challenging problems where multiple perspectives help.

Work toward continuous integration and trunk-based development as your team develops comfort with smaller changes and collective ownership. These practices require discipline and supporting skills, but provide maximum cognitive load reduction when implemented well.

Focus on finishing more and starting less. Limit work in progress to reduce context switching and coordination overhead. Celebrate completion over initiation.

Measure impact through delivery velocity and team confidence rather than just individual productivity metrics. Look for signs that engineers feel comfortable making changes, that knowledge is spreading across the team, and that the pace of delivery feels sustainable.

Conclusion

The choice isn't whether to invest in these practices. The choice is whether you'll address cognitive load deliberately or let it accumulate until your talented engineers burn out fighting their own tools.

Start with one practice that addresses your biggest pain point. If engineers are afraid to make changes, start with automated testing. If integration takes days, focus on smaller commits and collective ownership. If deployments are stressful, decouple them from releases.

As you work to reduce your team's cognitive load, keep these principles in mind:

• Small batches win: Break everything into the smallest possible increments
• Finish more, start less: Constrain work in progress ruthlessly
• Go slow to go fast: Invest time in practices that compound
• Clean as you go: Leave code better than you found it
• Do it right or do it twice: Quality isn't optional when you're moving fast
• Share the load: Distribute knowledge and responsibility across the team

But don't fool yourself into thinking you can skip the fundamentals and jump straight to organizational transformation. I've seen too many leaders try to implement empowered teams, platform strategies, and product discovery while their engineers can't safely change a single line of code.

These fundamentals serve as prerequisites for organizational transformation. Teams that struggle to build maintainable software can't effectively utilize better organizational structures, clearer product strategies, or improved customer feedback loops. The engineering practices create the foundation that makes everything else possible.

For organizational leaders, understanding these practices matters because your policies and expectations either enable or prevent their adoption. When you require detailed upfront specifications, you discourage TDD and incremental development. When you measure individual productivity over team capability, you undermine collective ownership and pairing. When you create approval processes that discourage frequent integration, you force teams toward risky, infrequent releases.

The goal isn't to have happy engineering teams that ship consistently while building the wrong things. The goal is to build teams capable of rapid, safe iteration so they can participate effectively in discovering and building the right things for your organization.

Fix the foundation first. The rest becomes possible once your teams can ship without fear.