Your Code as a Crime Scene

What if your codebase was a crime scene? Learn to use forensic analysis on your version control data to find the hotspots where technical debt is most expensive.

#1about 1 minute

Debunking the myth of speed versus quality in software

The common belief that improving code quality slows down development is a misconception that can be disproven with empirical data.

#2about 2 minutes

Applying psychology to understand software development

Studying psychology provides techniques to visualize software issues and communicate the impact of technical debt to non-technical stakeholders.

#3about 2 minutes

Using forensic psychology to analyze your codebase

Techniques from criminal profiling, like geographical offender profiling, can be adapted to track developer behavior and identify critical code hotspots.

#4about 1 minute

Identifying team dynamics through version control history

Version control data reveals organizational patterns, such as poor team-architecture alignment or knowledge silos like the 'lone wolf' developer.

#5about 2 minutes

Understanding the origin of the speed versus quality debate

The conflict between speed and quality arises from misaligned feedback loops, where new features offer immediate value while the costs of poor quality are delayed.

#6about 2 minutes

How AI makes managing technical debt an organizational necessity

The rapid code generation enabled by AI tools increases the volume of code so quickly that managing technical debt becomes essential for organizational survival.

#7about 2 minutes

Repurposing AI to simplify and understand existing code

Instead of just generating new code, AI's greatest potential lies in helping developers analyze, understand, and refactor complex legacy codebases.

#8about 4 minutes

How to convince management to invest in code quality

Developers can gain management buy-in for quality initiatives by presenting data-backed evidence and framing the problem in business terms like efficiency and time-to-market.

#9about 2 minutes

Establishing code quality as a key performance indicator

Poor code quality can waste up to 40% of engineering capacity, making it a critical metric that should be tracked as a KPI for the entire organization.

#10about 1 minute

The most important principle is to write code for humans

Since code is read far more often than it is written, the primary goal should be to create clear, understandable code for other developers, not just the machine.