It is fascinating to observe how, with increasing complexity and over time, software transitions into a state of being “a thing in itself”, where even the developers do not fully understand how it works, or more precisely, why it sometimes suddenly malfunctions, and prefer to minimally interfere with it, leading them to understand it even less over time, and it solidifies into what it is for years. This process is known as software rot or legacy paralysis.
However, bosses and the market demand development, so instead of fundamentally changing and improving something, developers add “bells and whistles” which grow alongside, rather than changing the core product. It’s well understood that diving into the core product might set you on a path leading to disappointments, deadline failures, layoffs, etc.
Interestingly, with the advent of AI, this problem will only intensify on one hand because the team will understand even less about how things work, but on the other hand, complexity can be managed better because AI can analyze complex matters more easily than a single biological brain.
For instance, AI could be used to create tests for existing code, as well as to perform anomaly detection and potential bug hunting, for creating documentation and explaining the code structure from simple to complex, and it might partly automate refactoring and detect performance bottlenecks.
I believe such AI solutions for working with legacy will soon be a major market.
Hi, I'm Rauf Aliev. 