armlet

Armlet, a MythX API client wrapper

Armlet is a thin wrapper around the MythX API written in Javascript. It simplifies interaction with MythX and JWT authentication. For example, the library wraps API analysis requests into a promise, merges status information with analysis-result information, and judiciously polls for results.

A simple command-line tool, mythx-analysis, is provided to show how to use the API. It can be used to run MythX analyses on a single Solidity smart-contract text file.

Installation

To install the latest stable version from NPM:

$ npm -g install armlet

If you're feeling adventurous, you can also install the from the master branch:

$ npm install -g git+https://[e-mail]/ConsenSys/armlet.git

The -g or --global option above may not be needed depending on how you work. It may ensure mythx-analysis is in your path where it might not otherwise be there.

Example

Here is a small example of how you might use this client. For demonstration purposes, we’ll set the credentials created on the MythX, you can use either the Ethereum address or email used during registration and the password you created:

$ export MYTHX_PASSWORD='AAAyyyyyyyy@*#!?'
$ export MYTHX_ETH_ADDRESS=0xdeadbeefdeadbeefdeadbeefdeadbeefdeadbeefdeadbeef

Then get the MythX analysis results with the promise returned by the exposed function:

const armlet = require('armlet')
const client = new armlet.Client(
  {
      password: process.env.MYTHX_PASSWORD,
      ethAddress: process.env.MYTHX_ETH_ADDRESS,
  })

const data = {
    "bytecode": "0x608060405234801561001057600080fd5b5060d48061001f6000396000f3fe608060405260043610603f576000357c0100000000000000000000000000000000000000000000000000000000900463ffffffff16806338d94193146044575b600080fd5b348015604f57600080fd5b50607960048036036020811015606457600080fd5b8101908080359060200190929190505050608f565b6040518082815260200191505060405180910390f35b600081600881101515609d57fe5b01600091509050548156fea165627a7a723058206f554b09240c9771a583534d72575fcfb4623ab4df3ddc139442047795fd383b0029",
};

client.analyzeWithStatus(
    { "data": data }, // required
    3 * 60 * 1000 // timeout - optional
  ).then(result => {
	  const util = require('util');
	  console.log(`${util.inspect(result.status, {depth: null})}`);
	  console.log(`${util.inspect(result.issues, {depth: null})}`);
  }).catch(err => {
    console.log(err)
  })

For statistical tracking you can tag the type of tool making the request using clientToolName. For example, to log analysis request as a use of armlet-readme, run:

client.analyzeWithStatus(
    {
	"data": data,
	"clientToolName": "armlet-readme"
    })
  .then(result => {
    console.log(result.status, {depth: null})
    console.log(result.issues, {depth: null})
  }).catch(err => {
    console.log(err)
  })

Improving Polling Response

There are two time parameters, given in milliseconds, that change how quickly a analysis result is reported back:

• initial delay
• maximum delay

The initial delay is the minimum amount of time that this library waits before attempting its first status poll. Note however that if a request has been cached, then results come back immediately and no status polling is needed. (The server caches previous analysis runs; it takes into account the data passed to it, the analysis mode, and the back-end versions of components used to provide the analysis.)

The maximum delay is the maximum amount of time we will wait for an analysis to complete. Note, however, that if the processing has not finished when this timeout is reached, it may still be running on the server side. Therefore when a timeout occurs, you will get back a UUID which can subsequently be used to get status and results.

The closer these two parameters are to the actual time range that is needed by analysis, the faster the response will get reported back after completion on the server end.

Good guessing of these two parameters reduces the unnecessary probe time while providing good response around the declared time interval.

So, how can you guess decent values? We have reasonable defaults built in. But there are two factors that you can use to get better estimates.

The first is the kind of analysis mode used: a "quick" analysis will usually be under two minutes, while a "full" analysis will usually be under two hours.

When an analysis request finishes, we provide the amount of time used broken into two components: the amount of time spent in analysis, and the amount of time spent in queuing. The queuing time can vary depending on what else is going on when the analysis request was sent, so that's why it is separated out. In addition, the library provides its own elapsed time in the response.

If you are making an analysis within an IDE which saves reports of past runs, such as truffle or VSCode, the timings can be used for estimates.

Read more about this Polling the API to Obtain Job Status in the MythX API Developer Guide.

Clients using armlet

• truffle-security
• sabre
• mythos

See Also

• example directory for some simple but runnable examples of how to use the client.
• openapi spec for details of the MythX API.
• MythX Developer and User Guide for more information
• MythX Discord channel to join the MythX community.