Sure, every developer has their own strategy of finding information within source code, from searching documentation to looking at tests, or asking the original authors when available. While there are many great code editors out there, they’re designed for writing code and are not made for navigating the implementation at hand. What is harder to address is how efficiently we can understand an existing implementation.

When it comes to writing code, we have a multitude of options to suit whatever workflow we desire. Why can't we take a step back and see how the components connect with each other, without constantly looking at every detail of code? Every line in the source code has a purpose and as software developers, we spend most of your time searching for those small pieces that are currently relevant. The problem here is not the poor abstraction of the language, but the high information density of code. But regardless of the language, if a project reaches a certain size, it's hard to keep a consistent mental model of the source code's structure. They reduce the overhead a software developer needs to keep in mind by abstracting away more and more limitations of the machine. Programming languages are constantly evolving. Programming languages are not the problem. New Developers will often have to spend days or even weeks browsing legacy code to familiarize themselves with the implementation before actually becoming productive. Consequently, more and more effort is spent investigating the current implementation while less is spent on writing new features. When a codebase grows, it becomes increasingly complex to track every addition. Working in teams allows us to solve harder problems, but it comes at a cost. Professional software development is often a team effort. But sometimes, it’s more difficult than we expect. As software engineers, we know that we have the skills and capacity to actualize all of our ideas.

Python code compiler