Encapsulation of asynchronous behaviour in distributed system of scripted autotests

This is an example how to create continuation-like monadic behaviour in C++11 (encapsulate async execution) which is used for specific example: writing asynchronous scripts for system of distributed autotests.

Async scenarions for distributed autotests

I needed to add autotests into continuous integration of client-side multiplatform product. Basic way to build autotesting system is to grab one of well-known frameworks (especially if we are talking about web solutions) and test product locally. Local testing environment simulates clicks buttons inside UI and gets the results right away. This is simple and good way except the case when you have truly cross-platform solution (win, mac, web, iOS, android, etc), custom rich UI framework and you want to perform tests on various kinds of systems at same time.

So I came up with remote distributed scheme for autotests. And inside this post there are some ways to make this scheme shine.

This post contains 3 main points:

  1. what is distributed system of autotests and why the one would need it (advantages / disadvantages)
  2. how to implement asynchronous scenarios of distributed autotests using continuation monadic style in C++11/14 (how to encapsulate asynchronous execution and do this the most compact way)
  3. how to integrate ChaiScript scripting into such system to make it more flexible


If you are interested only in ways of encapsulation of async interfaces in C++ you could skip this chapter and move on to PART 2.

Let’s build server which will control clients via sending events through separate web-socket connection. It is like performing tests of car when you use remote control. Next diagram shows main structure differences:

Distributed scheme for autotesting

Let’s discuss advantages at first:

Cover a lot of devices

In such scheme we could launch clients at any device (as we don’t need to setup local testing environment any more). Also it’s possible to run tests on clients under debugger and see what happens.

Write once – run on device park

We could launch tests on nightly builds using all available range of devices. When new test is written it will work on all devices (except case when test uses some platform specific aspect like push notifications, etc). When new device is added into the zoo there is almost zero overhead on setup of testing environment.

Analyze results

Compare test runs

We could export statistics from all tests into one server side storage and analyze all variety of test cases inside one analytical system. In my case it’s ElasticSearch + Kibana, but this could be any tools/storages you like to work with. Also the one could use autotests as profiling system: you could compare performance results measured during same tests (using different devices or different versions of target application).

Fast write, Fast run

Once again you need to write test only once to support all variety of platforms, operating systems, devices, etc. This is huge gain in time. Also when there is no need in custom setup of testing environment on device we could analyse more cases or spend more time writing new tests. Developers could run tests right on their builds without no need to upload them into some testing environment.

Finally – More stable CI

Performing tests on large zoo of devices, analysing not only green lights from tests but fine performance measurements, increased speed of test development and autotests integration – all this leads to more stable continuous integration.


Are there any disadvantages? First one is that you have to spend some time implementing such system. There are no boxed solutions as far as I know. But actually this is not the main problem. On application side it’s enough to create simple interface for accepting external commands received through socket. It could be only UI control commands (like click at x,y coords or is this element visible requests), or it could be some complex interaction with inner application model. Any way this is simple request-answer interface, which could be extended with periodic performance reports, sending last chains of inner events or even passing crash log reports to server.

What really is the problem: how we should write tests when test target is separated from test itself – how to write asynchronious tests?

For example, instead of general way when you deal with local tests and need to push some interface button you just need to call some method like button1.click() and thats all. But when you are dealing with distributed scheme you need to send some message to client – “push button1” and wait for answer. And we need to take into account that client might not even answer or our ui button might be hidden already and message could fail to execute.

finite state machine

To solve this first idea is to implement test on server side as finite state machine (link). Let’s assume test has a state which could be represented as some simple enumeration (or item from some set). Each state has listeners for client’s answers. And by each async operation we jump from one state to another. For example, for “click button” case we have two states – first: we send message and switch to second state, second: if we receive answer that button was successfully clicked we move on to next action.

If we have simple test of linear continuous actions we could just keep state as int and increment it performing each ‘step’ from current state to next one.

Everything could be implemented in such manner, but the problem is that if your tests are not so trivial the code will become unreadable mess of state jumps. Also there will be a problem to extract functionality into separate methods because state logic overlaps each other – finishing handler for last action inside some function block should move on to next state which is outside of scope.  You might look for ways to organise this structure and solve such ‘overlap’ problems but there is a better solution to solve this at once.



In ideal world I just want to keep writing the code of my test using old ‘synchronous‘ way as it is more simple, readable, etc. But I want it to work async way. To achieve this we need all power of C++11/14 because this will look like continuation monad in C++. What are continuations you could read here.

I want to write code using general sequential notation – but implicitly this code will produce a lot of functional handlers and totally encapsulate whole state machine inside. Yes, this is possible using modern C++!

Warm up example – simple login test:

This seems like normal local test but in reality under the hood this code creates state machine with message handlers which will be executed asynchroniously. Not only this is much shorter than defining states and handles manually, but also this is general way of doing things which is familiar for any auto tester.

The problem is that not all things could be transferred totally unchanged. Cycles, conditions and so on can not be written in such clean way except we introduce some preprocessing or DSL to make changes into asynchronous form. But let’s go step by step.


First big help here is auto keyword. When we execute request method like getElement in previous example the result is actually not a value, but a future. And as we need as compact syntax as possible and a lot of additional customisations, here will fit custom implementation. (There are a lot of ways to implement this – so any specific ways are optional and you could tune them as you like)

Here is part of such asynchronous value which is not yet acquired:

Main fields here: value – pointer to value which will be filled when answer from client will be received by server. state – corresponding state of test.  request – function which will send actual request to client. response – function with fill parse answer from server.

And so getElement function in previous code piece is not actually performing some actins but creates such ATRequestValue and fills it with corresponding request/response handlers as lamda functions:

In this example we try to find some UI element by name and some prefix (which is also string), and result is some identifier of type id.

Function addRequest is something like this:

Before we discuss scopes, jumpTo, etc – let’s first talk why arguments of getElement are not just strings, but ValueProvider<string>. We could have kept them as strings if all parameters had been predefined before test. We can’t do that because in reality the most of inputs are computed depending on previous results. Thats why we need value providers – functors which will return (provide) some value of predefined type.

Now we could pass lamda as parameter to getElement which will be executed later – for example:

Now let’s go back to place where getElement is called. Details on how exactly we form request and read response are not so important – what matters is how to make a chain of such calls. This is the place where we need scopes, elements, requests and jumps between them.

Let’s look at first simple case when we have only one scope (simple test with no branches or cycles). In such case we just need basic jump from one request to another (and gather them inside one scope’s storage to keep them alive).

In simple case to perform jump we need just increment test’s state and perform request of next RequestValue. To finish the magic we just need to assemble() things together into one state machine.

I’m skipping here the variadic part – ATRequestValue<T> could contain different types of values and so the list of requests should be able to hold variadic types – but may be this will be the topic of separate post.

In trivial case when we have only one scope running next item could be pretty simple:

And this enough to implement simple sequential chains without any branching. But we need more.


First problem arises when we want to add first conditional branch into our test’s logic. When we write something like:

This will not work because condition will be executed synchronously and text will not yet be received. So this code will crash. The solution is to create async_if implementation which will accept predicate and evaluate it at right moment when the text will be received. Also we need scopes here. They could be implemented as simple lists of run functions, but with some additional enter / exit handlers.

Optional stuff here – names for scope elements (for debugging). Also there are scope types – we will need them later.

Enter and exit functions return boolean values – when true is returned it means we had some asynchronious action called inside and should wait for result. If false is returned we could continue execution and move to next item in scope without waiting.

Also we need some structure which will contain all scopes and organise movement between them. We could use some tree or stack here.

Function enterScope here also returns boolean so it could stop synchronous execution and wait for next result in asynchronous chain.  Now our main function runNextItemInScope which goes to next item becomes slightly more complicated – it’s a cycle which will call elements in current scope one by one until any of them signal that we have to wait by returning true from it’s run(). When there are no more items in scope we pop upper scope from scope stack and continue execution there using same pattern. And finally when scope has no parent (stack has only 1 item) we just stop.

It’s great that in such scheme we could implement not only “if” branch but cycles too. But let’s start with async_if:

So this function actually executes both then/else branches right away! But this execution only creates functional handlers which will be called later. We pass both branches and predication as parameters here. Thanks to C++11’s lambda syntax it’s possible to write such code:

I have to admit this is not so pretty as simple if and this is very sad. Of course this is much better than writing async handlers straightforward way, but this still is not perfect. Optional way is to make some preprocessing eliminating boilerplate functional wrapping, or even make some DSL for autotests. Using additional scripting overlay may ease the pain a bit (will be discussed at part 3).


Same way we could define while cycle.

Here we set custom exit procedure which checks condition and reenters scope if it was not satisfied.  Usage example could be the following:

We could also introduce continue operator. This is the place where we need to know the type of scope. Logic is simple – we go up through scope stack until we find cycle scope, and then we reenter that scope.

Now we could have any kind of nested structure like async_continue inside async_if which is inside async_while.


Async foreach is a bit tricky as you have to iterate over something which is not yet obtained. But whole trick is that you have to use data provider instead of data itself. Provider is a function which gives array – and you could access it by index or iterator. There are a lot of freedom here so I put here only basic start example for some vector and indexed access:

Here we redefine both enter/exit handlers to create an iterator. Cycle body is called with parameter which is a value provider once again.

Usage example:

This sample clicks all tabs inside some menu bar.


There are a lot of cases when between asynchronous requests we need to perform some synchronous actions (like printing something to log). Of course it cannot be written general way because in that case it will be executed at test’s assemble time when we only set handlers and yet have no data. As one way to solve this we add additional function – async_do.

So we just wrap functional body into scope element and insert it into scope. We could introduce here a lot more of helper functions such as async_set or async_copy. They could also assign the result to some variable or do something else.

Usage example of async_do could be found in part 3.


Second way to perform synchronous actions is to insert some functional checks right inside request values.

First one to add is special assert which check that async request went well:

To implement such functions we only need to create array of functional checks inside ATRequestValue and add such methods as:

I use fluent interface pattern here – this is optional of course.

You can find usage examples of such checks in part 3.

Also we could add function waitFor(interval, condition) which will make periodic requests until provided functional condition will be fulfilled.

I think you already got a lot of ideas how to expand this approach. So let’s move on to the last chapter.


Final step to make our life sweet enough is addition of some scripting language. Advantages?

  • there will be ability to add new tests without rebuilding server (actually we even could not restart it)
  • we could write new tests faster
  • whole thing becomes more flexible
  • automation testers feel more comfortable as it’s not C++ (yes, that’s a plus 🙂 )


Here comes our new hero – ChaiScript. At first I was thinking about lua, but after reading ChaiScript’s docs I decided that language has more that enough flexibility to cover autotests needs. Main advantage of ChaiScript is that it is made for C++ integration and so the one could expect very fast way to add the scripting into the app. ChaiScript is relatively new but mature enough. It has relatively good documentation.

It took me only one day to integrate it into testing environment.

Whole ‘scripting part’ implementation at the moment is just 300 lines of code. And it gave the ability to write all asynchronous tests completely inside separate script files.

Base syntax of ChaiScript is very similar to C++ or JS. In simple case there are only two points the one should change to make C++ test work as script:

Actually syntax for lambdas is even a bit better as too much square brackets may make things less readable.

Example of test:

This is test which every 3 seconds selects min or max value on some plotter, performs some checks and prints it to log. I hope now you can feel the benefits of encapsulation of async requests.

As for interface implementation between scripting and C++ – it is pretty simple. Not only you could export functions and object methods to ChaiScript, but you could also export custom type conversions, complex types and so on. I will not provide implementation details here as it will make the post too big, but you could get the idea from this cheatsheet.

One minor disadvantage: as far as I know there is no default parameter capture modes for lambda functions inside ChaiScript at the moment. If you work with a lot of small functions this could be an improvement to have syntax like fun[=](){ …. } which is not available at the moment. Or even make is as default behaviour. I hope @lefticus will add this in future.

Anyway, ChaiScript seems like a nice solution for me at the moment.


Encapsulation of async execution using modern C++ gives ability to create distributed solutions. This post shown how such technique could be used for creation of custom system of distributed autotests. Scripting could be added into such systems to increase flexibility.