<p>A classical result from automaton theory shows that even the problem of finding minimum-size DFA consistent with given positive and negative samples is NP-hard. This was shown by Gold in 1978. Your problem is much harder, as in practice you would have to learn data relationships in addition to any finite-state control of the system interface.</p>

CC BY-SA 3.0
2012-08-14T00:50:45.733Z
thanks - I thought that would be the case. It might be possible to add hints or to fine tune a manually developed prototype mock.
velniukas
CC BY-SA 3.0
2012-08-14T07:26:46.467Z
Additionally, if you watch network traffic but are not allowed to generate it you get only positive samples... :D
antti.huima
CC BY-SA 3.0
2012-08-13T10:49:47.260Z
2012-08-13T10:49:47.260Z
antti.huima
<p>I'm investigating whether it is feasible to be able to learn a system interface by watching network traffic (assuming the usual problems are solved e.g. encryption etc)</p>

<p>I haven't been able to find any literature on this - but there might be a cross-discipline piece of work which could assist.</p>

<p>The main purpose here would be to allow complex enterprise systems to be refactored independently. A simple replay of data doesn't always work because we want to learn the request-response interface - though e.g. something like a market trade having multiple fills is an example of very complex orchestration that might make the whole thing unpractical.</p>

<machine-learning><network-modeling>
Is it possible to create a machine learning classifier to generate Mock interfaces for systems testing?
velniukas
# Is it possible to create a machine learning classifier to generate Mock interfaces for systems testing?

(Asked by: username_0 on 2012-08-12)

I'm investigating whether it is feasible to be able to learn a system interface by watching network traffic (assuming the usual problems are solved e.g. encryption etc)

I haven't been able to find any literature on this - but there might be a cross-discipline piece of work which could assist.

The main purpose here would be to allow complex enterprise systems to be refactored independently. A simple replay of data doesn't always work because we want to learn the request-response interface - though e.g. something like a market trade having multiple fills is an example of very complex orchestration that might make the whole thing unpractical.

**Tags:** machine-learning, network-modeling


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## 1 answers

### Answer by username_1 on 2012-08-13. Score: 4 | Accepted answer

A classical result from automaton theory shows that even the problem of finding minimum-size DFA consistent with given positive and negative samples is NP-hard. This was shown by Gold in 1978. Your problem is much harder, as in practice you would have to learn data relationships in addition to any finite-state control of the system interface.

> **Comments:**
> **username_0**: thanks - I thought that would be the case. It might be possible to add hints or to fine tune a manually developed prototype mock.  
> **username_1**: Additionally, if you watch network traffic but are not allowed to generate it you get only positive samples... :D  

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