Traces in the Code
What Codescene Tells Us About Our Behavior
There’s a phrase I often repeat to my teams:
“Code doesn’t lie. But it doesn’t tell the whole truth either.”
Some time ago, while we were trying to figure out why the same bugs kept popping up in the same module, we realized the issue wasn’t just about missing tests or flawed design patterns.
It was about repeated human behavior, rushed decisions, overlapping responsibilities, and that one library everyone avoids touching if they can.
And yet, we had no tools to see any of that.
We were reading the code, but not the story it was trying to tell us.
The Problem: Making Decisions in the Dark
As a CTO or tech lead, how many times have you had to decide:
whether to refactor or rewrite a module,
who should own a particular part of the system,
when (and if) to invest in tackling tech debt?
And how often have you done that without real data, relying on gut feeling, anecdotal memory, or the opinion of the last developer who touched that code?
It’s frustrating. And risky.
Because we’re making decisions in the dark, when in fact we’re surrounded by clear signs, if we know where to look.
The Solution: The Repository as a Crime Scene
The insight behind tools like Codescene is both simple and powerful:
Your code repository is a crime scene.
It’s not just a storage of source files, it’s a trail of clues, commit after commit, merge after merge, telling you what’s really going on in your system.
Like a forensic detective, you can investigate:
who made changes and when,
how frequently a file is touched,
whether changes were isolated or involved multiple people,
whether the area shows signs of conflict, instability, or rushed decisions.
Some files are quiet, rarely modified, safe neighborhoods.
Others are noisy, fragile, and heavily trafficked, hotspots with hidden risks.
And those are the areas where something’s happening that your org chart isn’t showing.
Codescene turns these clues into actionable maps and insights:
high-risk hotspots,
declining code health,
co-evolving modules (those that often change together),
unintentional team coupling.
That’s what makes Codescene so valuable:
It doesn’t just tell you how your code is written, it helps you understand why it got that way, and what behaviors led to it.
When you begin to connect those patterns with how your teams are structured, it changes everything.
How We’re Using It
We’re navigating a common challenge in many product companies:
a monolith that served us well for years… but is now holding us back.
Each product team inherits part of that legacy codebase, and they’re responsible for both:
delivering value today, and
designing a better, more modular system for tomorrow.
Codescene helps us:
prioritize where to refactor, based on risk and frequency, not just gut feeling;
see who really owns a module, beyond what org charts say;
facilitate conversations around code quality and decision-making, grounded in evidence;
bring data into roadmap planning, making the case for tech debt investments with clarity and confidence.
It’s not magic.
But it’s one of the few tools that turns code from a liability… into a lens.
What Comes Next: From Code Behavior to Team Health
Tools like Codescene help us see the system through the lens of code, revealing where risk accumulates, where effort concentrates, and where patterns of fragility emerge.
But understanding code behavior is only half the story.
The other half?
👉 Understanding the teams behind it.
In the next article, we’ll look at how we measure:
the operational metrics of our teams (yes, we’ll talk DORA),
their health signals (like cycle time, work in progress, flow efficiency),
and their actual impact, not just their output.
Because if the repository tells us what happened,
the team metrics help us understand why, and how we can help things go better next time.
Stay tuned. And as always, feel free to write me if you're already exploring this topic, or just want to share how you're approaching it in your own company.
Want to Talk About It?
If you’re working with a legacy monolith, struggling to define team ownership, or just looking for ways to bring more evidence into your roadmap decisions… this topic might resonate with you.
Drop me a message, I’d love to share experiences or just exchange notes.