Software Engineering as a discipline often doesn’t feel very mature. Even though it is an engineering discipline, much comes down to gut feeling and reinventing the wheel. Most engineering disciplines have a more scientific and data-driven approach than software, but a lack of data leads to gut feeling. I’ve discussed Tech Debt in ♻️ Sustainable Software, but I think part of the issue is insufficient data. Or at least insufficient understandable data. If it’s easier to visualise and track the true cost of tech debt, the conversation will change I think.
Static Code Analysis tools like SonarQube and Detekt exist that point out Software Complexity like Cyclomatic Complexity or too many input parameters. The downside is that these tools are usually limited to pointing out very localised complexity.
There are tools that take a more holistic view on tech debt and analyse repositories as a whole, such as CodeScene. More information on the idea behind CodeScene can be found in 🧏 Listening Notes - The Code Quality Advantage. The presentation makes a good case on the value of code quality, and visualising focus areas using a combination of a health metric with change frequency. There is still one aspect missing in a tool like this, which is the interconnectedness of tech debt.
The idea of 📏 Complexity is to measure code complexity and give visibility on tech debt with the added dimension of interconnectedness. 🧅 Software Systems Are Like Onions describes how tech debt can spread through software systems, showing that this is an underrepresented problem area in existing tooling.