Earlier quoted context omitted.
I'm suggesting that you take an existing system and build up a corpus of request/response pairs. Then you use the LSTM to build a prediction model so that given a request it will tell you that the current production system will produce the following sql statement and this http response. Once the LSTM's output is indistinguishable from your current production system , for all use cases, then you replace the production…
What if your app has literally any mutable state? Registering accounts, posting comments, etc. Also I'll bet you that your neural net is > 100x slower than straight line code.
For session based variables? Not sure, either it all becomes stateless and the code has to read everything from storage for each reqeuest .... or maybe the lstm is able to model something like an entire user session and remember the stuff that the original app would have put in the session.
That Andrej Karpathy article that I linked to two comments above ... he pointed out, in a different blog post, that regular neural networks can approximate any pure function. Recurrent neural networks like the LSTM can approximate any computer program. It is because they can propagate state from step to step that allows them to do this.
As far as it being 100X slower, well at a certain point I will be willing to take your money :)