Judgment in software development is clutch—it’s what separates the code monkeys from the architects. (Grok)
Role of judgement in software development
In software development, judgment is about anticipating more than just today’s requirements—it’s about preparing for tomorrow’s unpredictability. You’re coding not only for current specifications but also for future challenges: will the system scale under load, will it fail unexpectedly, or will it create problems later? This foresight requires balancing intuition with analysis, much like strategizing in a game where the next move isn’t fully visible. Development involves a constant tension between order—such as clean code and design patterns—and chaos, driven by deadlines, bugs, and changing requirements. Judgment provides the balance, guiding you to enforce structure when it’s needed and to tolerate imperfection when time or resources demand it, adapting to uncertainty rather than resisting it.
Judgment goes beyond technical decisions; it reflects the developer’s character. It’s not just about expertise but also about personal qualities—patience to refactor a messy module, humility to recognize a flawed approach, and courage to challenge impractical demands. The code you write mirrors these traits, revealing both your strengths and limitations through the choices you make. Whether it’s opting for a quick fix or investing in a robust solution, these decisions shape not only the software but also how you grow as a professional, tying technical skill to individual integrity.
Beyond numbers
In software development, relying solely on metrics can mislead, but avoiding them entirely risks paralysis. For instance, skipping numbers on testing coverage might leave a codebase with no clear standards, while obsessing over a metric like cyclomatic complexity can waste time on stable, unchanged classes that don’t need refactoring. It’s better to have an imperfect measure, like tracking bug frequency over weeks, than no measure at all, as this reveals whether a system truly “works” or just limps along with constant fixes. Numbers provide a starting point, but without judgment to interpret them and adjust for deteriorating systems or shifting priorities, they lose their value, leaving decisions to navigate the gray area of “it depends”.
Judgment also helps developers separate a task’s inherent business complexity from accidental complexity introduced by technical choices. For example, a payment processing feature might be complex due to regulatory requirements—unavoidable business rules—but poor judgment, like overengineering with an unneeded microservices split, can pile on accidental complexity, such as latency from extra network calls or debugging across services. Metrics like deployment time or error rates can hint at this, but only judgment discerns whether the numbers reflect the task’s nature or self-inflicted issues. By logging decisions—like choosing a simpler stack—and reviewing outcomes, developers can refine their ability to minimize unnecessary complexity, focusing effort where the business demands it most.
Judgement calls
Every day, developers face judgment calls that weave together trade-offs and team dynamics. Prioritizing speed, quality, and cost means knowing when to release a functional feature rather than perfecting it endlessly—for instance, deciding whether to optimize an algorithm now or wait until it slows the system. It’s also about understanding the team, choosing tools like a framework based on their skills and the company’s existing investments, rather than chasing the latest trend. Debugging often relies on instinct—like suspecting a race condition—when logs alone aren’t enough, while scope creep demands the discipline to say “no” or “later” to keep the project on track. Tech debt, meanwhile, is a calculated risk: judgment determines when a shortcut makes sense for a deadline and when it’s wiser to address it early, preventing a cascade of issues later. Below examples highlight the daily decisions developers navigate worldwide.
Framework Adoption
When building a web app, Next.js offers efficient React rendering and long-term scalability, but if your team consists mostly of junior developers skilled in vanilla JavaScript and jQuery, judgment is key. You must decide between adopting Next.js, which requires a learning curve, or using Express.js, which aligns with their current abilities. A practical choice might be to select Express.js for faster delivery and plan to train the team on Next.js later, balancing immediate needs with future growth.
Microservices or Monolith
Splitting a legacy app into microservices allows independent deployments and improved scaling, but with a team of five developers—only two familiar with Docker—judgment favors a monolith. A monolith is simpler to manage with limited operational skills, keeps latency lower by avoiding network calls between services, and reduces code complexity since there’s no need to coordinate multiple interdependent components. Pushing microservices could overburden the team, introducing challenges like Kubernetes configuration they can’t handle, along with higher latency from service-to-service communication and increased complexity in debugging distributed logic.
Testing Depth
Deciding between unit tests for every function or integration tests for major workflows depends on team capability. If your team excels at test-driven development, you might aim for 80%+ unit test coverage to catch edge cases early. However, if many struggle with writing mocks, judgment favors integration tests—covering fewer scenarios with broader scope—to suit their skills, even if it means some unit-level issues might slip through.
Legacy Code Overhaul
Faced with a tangled PHP codebase—tightly coupled and untested—a full rewrite in Go might seem appealing, but judgment considers the team: one developer knows Go, while three are experienced in PHP. Rewriting would delay progress significantly, so a better approach is incremental refactoring—adding tests and decoupling modules—leveraging their PHP expertise while gradually improving the code’s structure.
Caching Strategy
Redis could accelerate slow database queries for your API, but if only one busy team member has experience with it, judgment favors a simpler in-memory cache, like a Kotlin HashMap. Though less efficient than Redis, it’s faster for the team to implement, meeting current needs without overtaxing their limited expertise, while leaving space to transition to a more robust solution later.
Deadline-Driven Shortcuts
The sprint’s closing, and the user authentication module still relies on a temporary database table. Do you leave it as-is to meet the deadline, or push the sprint to migrate it to a permanent schema? If your team has a strong track record of addressing technical debt post-launch, judgment opts for shipping now, confident they’ll refactor later. With a less reliable team, delaying is safer—otherwise, the makeshift table lingers, risking data inconsistencies down the line.
Improving judgement
1. Analyze Past Decisions with Metrics
After a project, measure the results of your choices. For example, if you chose a monolith over microservices, track deployment frequency (once daily vs. hourly) and bug rates (5% higher due to coupling) using tools like Jira or Sentry. Numbers make the impact clear, like a 20% slowdown from skipping Redis caching, helping you learn what works. Review quarterly.
2. Shadow the Masters
Work with an experienced developer on a complex task, such as optimizing a query, and study their reasoning—like why they picked a JOIN over a subquery. You’ll pick up their decision-making process which builds your own judgment.
3. Use Experimental Builds for Data
Build a small system twice—like a CRUD API with Flask and then Spring Boot—and compare build time (4 hours vs. 8 hours) and scalability (100 requests/second vs. 300 requests/second). Testing both options gives you concrete data, so you can compare metrics, not feelings.
4. Formalize Judgment with Decision Criteria
Before deciding, like choosing PostgreSQL over MongoDB, list factors—schema structure, team experience (SQL: 80%, NoSQL: 20%), query needs (OLTP vs. OLAP)—and score them, then check results later with post-mortems. A clear score (Postgres: 85, Mongo: 60) helps you make reasoned choices.
5. Broaden Technical Exposure
Learn new tools on a regular basis, such as Kafka for event streams, and build a simple pipeline (1000 messages, 500 messages/second throughput) to understand its trade-offs compared to options like RabbitMQ. Knowing more tools helps you make informed decisions.
6. Know Your Team
Before starting a project, document each team member’s experience with key technologies and review how those skills align with the work, such as a backend-focused sprint requiring strong database or API expertise. After, check metrics like code review turnaround, feature delivery velocity, or tech debt tickets to see if gaps affected progress. Recognizing that skill gaps bogged down tasks helps you adjust future plans, like pairing novices with experts.
7. Iterate Under Pressure
In a short deadline, like 48 hours, pick a solution—such as in-memory caching—deploy it, measure the result (latency from 200ms to 50ms), and adjust if needed (e.g., memory errors). Working under constraints teaches you to balance speed and quality effectively. Document the fix later.
8. Read The Classics
Study foundational books like “Design Patterns” or “Refactoring” which offer proven principles to improve your decision-making skills. Apply a concept—such as replacing a switch statement with polymorphism—and measure the impact, like reducing cyclomatic complexity from 15 to 8. This gives you concrete data to understand why these practices matter
Software development is a gamble
Real-world decisions, like choosing in-memory caching over Redis, are gambles. Judgment grows when you make a choice and act, not when you overanalyze. Software is never truly “done”—it’s a living system shaped by human decisions. Good judgment means accepting imperfection, understanding that every line of code balances the ideal against the practical. You won’t have all the information upfront, so select a caching strategy, deploy it, and refine it later. Focus on reviewing actual results, not theoretical possibilities, and avoid stalling in pursuit of perfection.