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We have decided to not focus on Verifpal generating software
implementations from models and to instead dedicate our efforts to other
aspects of Verifpal.

As reported by Friedrich Wiemer:
https://lists.symbolic.software/pipermail/verifpal/2020/000319.html

Today I received a bug report from Tom Roeder, in which he demonstrated that the following models, Model A and Model B, caused a crash in Verifpal. Let’s look at Model A:

```
// Model A
attacker[active]

principal Client[
  knows private a, b, c, d, e, f
  gg = CONCAT(a, b, c)
  h = CONCAT(gg, d, e)
]

Client -> Server : h

principal Server[
  gc, dc, ec, fc = SPLIT(h)
  ac, bc, cc = SPLIT(gc)
]

queries[
  authentication? Client -> Server: h
]
```

Here, the cause is obvious: `h` is being split into four values despite being comprised of a concatenation of three values. This should not crash Verifpal, but should reasonably either be defined behavior or throw an error. However, if we look at Model B:

```
// Model B
attacker[active]

principal Client[
  knows private a, b, c, d, e, f
  gg = CONCAT(a, b, c)
  h = CONCAT(gg, d, e, f)
]

Client -> Server : h

principal Server[
  gc, dc, ec, fc = SPLIT(h)
  ac, bc, cc = SPLIT(gc)
]

queries[
  authentication? Client -> Server: h
]
```

Here Verifpal crashes despite the model being sane. This is because the active attacker renders it non-sane by replacing `h` with CONCAT(any, three, values), again triggering the crash when `h` is split into four values despite being a concatenation of three values.

Thus Tom showed that the bug caused a crash in Verifpal in both non-sane (Model A) and sane (Model B) models.

At this point, it became clear that it was important to clearly define Verifpal’s behavior when faced with such instances. I have decided to define it such that any extraneous values are rewritten to `nil`. So, for example, in Model A, on this line:

```
gc, dc, ec, fc = SPLIT(h)
```

`fc` will be rewritten to the `nil` value. This seems to be the most sensible defined behavior for this sort of scenario.

The model is wrong and doesn't actually show a counter-example.