u/InvestmentSlow4983

I have been trying to figure out how ai affects software development over last year and I have learnt about software management/maintenance a little bit basically I have surface knowledge such as integration tests and yaml files that run actions on defined triggers, so I decided to build an app that will automate this process but the problem is don't know much about this domain yet can you people guide me

Currently I have just been able to automate the part of security injecting codeql , semgrep and gitleaks and fixing the alerts that have been generated by them but i don't think that is enough cause these tools are notorious for finding out false positives and alerts on code paths which can never be subjected to SQL injection

I have been trying to figure out how ai affects software development over last year and I have learnt about software management/maintenance a little bit basically I have surface knowledge such as integration tests and yaml files that run actions on defined triggers, so I decided to build an app that will automate this process but the problem is don't know much about this domain yet can you people guide me

Currently I have just been able to automate the part of security injecting codeql , semgrep and gitleaks and fixing the alerts that have been generated by them but i don't think that is enough cause these tools are notorious for finding out false positives and alerts on code paths which can never be subjected to SQL injection

Lately I've been wondering about something.

AI is making it easier than ever to generate code, and it feels like teams are producing more code than ever before.

But are we getting any better at understanding and maintaining it?

A lot of my time doesn't go into writing new code. It goes into figuring out how existing systems work, debugging issues, and all the things that happen after the code is written.

For those working on production systems, what consumes most of your time today?

Does it feel like the gap between the amount of code being created and the team's understanding of that code is getting larger, or is this just the same challenge software teams have always had , or is ai amplifying this problem more ?