I Was Wrong About How I Spend My Coding Time

After tracking my own coding patterns for 30 days while building FlouState, I discovered I was completely wrong about how I spend my time. Here's what the data revealed.

The Time Perception Problem in Software Development

Ask any developer how they spend their day, and you'll hear: “Mostly debugging,” “Fixing other people's code,” or “Fighting with broken builds.” But when we analyzed actual coding patterns, the reality was starkly different.

I spent less than 1 minute per day debugging, yet it felt like hours. This perception gap has serious consequences for career satisfaction, imposter syndrome, and burnout.

Why Developers Misjudge Their Time: The Psychology

Cognitive biases affect how we perceive time:

• Negativity bias: Frustrating tasks (debugging) create stronger memories than pleasant ones (creating)
• Flow state amnesia: When coding new features, 3 hours feels like 30 minutes
• Recency effect: We overweight the last task of the day (often debugging before leaving)
• Attention density: High-focus debugging feels 3-4x longer than it actually is

Real Productivity Patterns (Based on Data)

How to Optimize Your Coding Time (Evidence-Based)

1. Time-box debugging sessions

Set a 25-minute timer when debugging. If not solved, switch to a different task and return later. This prevents the “debugging black hole” where 3 hours disappear.

2. Batch similar work types

Group all refactoring together, all feature creation together. Context switching between work types creates mental overhead and reduces productivity.

3. Track your actual patterns

You can't optimize what you don't measure. Even one week of data will reveal surprising patterns about when and how you're most productive.

4. Protect your flow state

Identify when you naturally fall into deep focus and protect those windows. Schedule meetings and shallow work during your lower-energy periods.

How This Changed My Relationship with Imposter Syndrome

For years, I felt like an imposter. “Why am I always fixing bugs instead of building?” I'd ask myself after another exhausting day. Every debugging session felt like evidence that I wasn't good enough.

Then I saw the data: 54% creating new features, 32% exploring codebases, 13% refactoring, and only 1% debugging. The frustration from that tiny 1% had completely warped my perception of productivity.

That realization changed everything:

• I stopped the negative self-talk about “spending all day debugging”
• I started optimizing for the work I was actually doing - creating and exploring
• I recognized that exploration time wasn't procrastination - it was preventing future bugs
• I scheduled my most creative work during my peak productivity times instead of wasting them on shallow tasks

The data didn't just track my time. It changed how I see myself as a developer. I'm a creator who occasionally fixes bugs, not the other way around.

What I Believed vs What the Data Showed

How I Tracked This (Methodology)

I built FlouState to automatically detect work types in VS Code without any manual input:

• Debugging: Active debug session in VS Code (breakpoints, step-through, watch variables)
• Creating: High ratio of lines added (>70% new code vs deletions)
• Refactoring: Balanced adds and deletes (30-70% range) - restructuring existing code
• Exploring: Many file views with few edits - reading and understanding codebases

The extension tracks in 30-minute windows, aggregating file changes, git activity, and debug sessions. It never captures code content - only patterns and metadata.

Is it perfect? No. But after 30 days, the patterns matched my intuition once I got past my initial perception bias. The 1% debugging number felt shockingly low at first, but reviewing my actual work confirmed it.

Action Steps: Start Tracking Your Reality

1. Track your coding for just one week
2. Note what type of work you're doing (creating/debugging/refactoring/learning)
3. Record your energy levels throughout the day
4. Compare perception vs reality at week's end
5. Adjust your schedule based on actual peak times

Tools for Tracking Developer Time

Several approaches exist for tracking coding patterns:

• Manual logging: Simple but requires discipline
• Git commit analysis: Shows output but misses exploration time
• IDE plugins: Automatic tracking within your editor
• Time tracking apps: General purpose but lack coding context

The key is finding a method that works automatically without adding friction to your workflow. Manual tracking often fails because it interrupts flow state.