What I Look for First When I’m Dropped Into an Unfamiliar Codebase

Being dropped into an unfamiliar codebase can feel overwhelming, especially when it’s large, under-documented, or has been touched by many hands over time. Over the years, through coursework, personal projects, and real-world work, I’ve developed a mental checklist that helps me orient myself quickly and responsibly.

This isn’t about finding bugs right away or refactoring everything in sight. It’s about understanding the shape of the system before trying to change it.

Here’s what I look for first.

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1. Entry Points: Where does this thing actually start?

The very first question I ask is simple:

What code runs first?

Depending on the system, this might be:

• a main() function
• a server entry file
• a framework bootstrap file
• a build or deployment script

When I worked on FaithTech’s ShareBibles project, identifying the initial execution path was critical. Once I understood how requests entered the system and which components were responsible for handling them, the rest of the codebase became far less intimidating.

Entry points tell you:

• how control flows
• what assumptions the system makes at startup
• which parts of the codebase are “core” vs. peripheral

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2. Build & Run Instructions: Can I get this running locally?

Before I try to understand everything, I want to know if I can run the system end-to-end.

I look for:

• a README
• setup scripts
• environment variables
• expected versions of tools or runtimes

If the build process is unclear, that’s already valuable information. On Thrive Vineyard’s website, much of my early work involved making sure the project could be reliably built and run across environments, not just for me, but for future contributors.

If I can’t run it, I can’t safely change it.

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3. Data Flow: What moves through the system?

Once it runs, I try to trace:

• where data comes from
• how it’s transformed
• where it ends up

This applies whether it’s:

• user input in a web app
• database queries
• API responses
• datasets in a machine learning pipeline

In several higher-level CS courses, especially those focused on systems and data-heavy applications, I learned that understanding data flow often reveals implicit architecture decisions that aren’t written down anywhere.

If you understand the data, you understand the system.

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4. State Management: What persists, and where?

State is where complexity hides.

I look for:

• global state
• shared mutable data
• caches
• database-backed state
• client vs. server state boundaries

On web projects in particular, understanding how state is managed, and why, is often more important than any individual function or component.

Poorly understood state leads to subtle bugs. Clearly defined state boundaries make systems easier to reason about and extend.

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5. Tests (or the lack of them)

Tests tell you two important things:

1. what the original authors cared about
2. what they were afraid of breaking

If tests exist, I read them early. They often explain intended behavior better than comments.

If they don’t exist, that also shapes how cautiously I move. In some cases, I’ll write small exploratory tests or scripts just to lock down behavior before making changes.

Either way, tests (or their absence) inform how I should work in the codebase.

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6. Naming and Structure: What story is the code telling?

Finally, I zoom out.

I look at:

• directory structure
• naming conventions
• module boundaries
• consistency (or inconsistency)

Good naming and structure reduce cognitive load. Inconsistent or unclear structure tells me where the system may have grown organically or under time pressure.

This doesn’t mean the code is “bad”, it just means I should tread carefully and try to match existing patterns before introducing new ones.

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Why this matters

This approach isn’t about speed for its own sake. It’s about being a good collaborator.

Before changing a system, I want to understand:

• why it exists
• how it evolved
• what tradeoffs were made

This mindset has helped me move between academic projects, volunteer work, and production systems without breaking things unnecessarily, and it pairs directly with how I approach my own projects and case studies today.

Understanding comes first. Code comes second.

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If you’re interested in how this mindset shows up in practice, many of the projects on this site include case studies where I walk through these exact steps in real systems.