Static analysis

One distinctive feature of Marlowe – probably its most distinctive feature – is that we can analyse contracts, and deduce properties of them, without running them.

We can check, in advance of running a contract, these properties:

  • Partial payments: i.e. payments when there was not enough money in account.

  • Non positive deposits: under which the contract asks for a value which is negtive or zero.

  • Non positive payments: payments of 0 or a negative amount.

  • Shadowing of Lets , where two Lets set the same identifier in the same execution path.

In the rest of this tutorial we will focus on the first of these, which is the worst kind of error. It is in this case that a payment fails, because there is not enough money in the contract (or more strictly in the account) to make a complete payment.

An example

Let us look at this example, in Blockly

An example contract in Blockly

and in pure Marlowe

An example contract in Marlowe

The contract first requires a deposit from Alice of 1 Lovelace, and then asks Bob to make a choice (called bool) of 0 or 1. The contract then pays this choice plus one to Bob from Alice’s account.

So, we can see that while the contract works OK when Bob chooses 0, there won’t be enough in the contract to pay him if he chooses 1. Our analysis, which is built into the SIMULATION tab in the Marlowe Playground, can diagnose this:

Failed analysis

This shows that the error occurs when

  • Alice has made the deposit, and

  • Bob has chosen the value 1.

and that the error generated is a TransactionPartialPay: in this case Bob only receives a payment of 1 instead of 2.

If we modify the contract so that Alice makes an initial deposit of 2, then the analysis – this time invoked from the Blockly view – will succeed:

Successful analysis

Under the hood

Just to re-iterate: the effect of this analysis is to check every possible execution path through the contract, using a symbolic version of the contract. This gets passed to the Z3 SMT solver, which is a state-of-the-art automated system for deciding whether logic formulas are satisfiable.

If the analysis is not successful, i.e. if there is a way of making the contract fail a payment, then the system will also give an example of how it can go wrong, and present that to the user. Users can then fix the problem, and check it again.

Next steps

In the next few months we will look at how to present the results of our analysis in a more “user friendly” way, as well as broadening out the scope of our work.

Use the analysis button in the Marlowe Playground to analyse some of the contracts that you have already written. If the analysis fails, can you see why, and correct the contracts so that they do not fail.