I think the mention of 'spaghetti code' is a red herring from the author. If the output from an algorithm cannot be defined precisely as a function of the input, but you have some examples to show, that's where machine learning (ML) is useful.
In the end, ML provides one more option to choose from. Whether it works or not for you depends on evaluations and how deterministic and explainability you need from the chosen algorithm/option.
The thing that struck me is if RNN is the right choice given that it would need to be trained and we need a lot of examples than what we might have. That said, maybe based on known 'rules', we can produce synthetic data for both +ve and -ve cases.
In the end, ML provides one more option to choose from. Whether it works or not for you depends on evaluations and how deterministic and explainability you need from the chosen algorithm/option.
The thing that struck me is if RNN is the right choice given that it would need to be trained and we need a lot of examples than what we might have. That said, maybe based on known 'rules', we can produce synthetic data for both +ve and -ve cases.