Author Archives: Átila Neves

The importance of making the test fail

TDD isn’t for everyone. People work in different ways and their brains even more so, and I think I agree with Bertrand Meyer in that whether you write the test first or last, the important thing is that the test gets written. Even for those of us for whom TDD works, it’s not always applicable. It takes experience to know when or not to do it. For me, whenever I’m not sure of exactly I want to do and am doing exploratory work, I reach for a REPL when I can and don’t even think of writing tests. Even then, by the time I’ve figured out what to do I usually write tests straight afterwards. But that’s me.

However, when fixing bugs I think “TDD” (there’s not any design going on, really) should be almost mandatory. Almost, because I thought of a way that works that doesn’t need the test to be written first, but it’s more cumbersome. More on that later.

Tests are code. Code is buggy. Ergo… tests will contain bugs. So can we trust our tests? Yes, and especially so if we’re careful. First of all, tests are usually a lot smaller than the code they test (they should be!). Less code means fewer bugs on average. If that doesn’t give you a sense of security, it shouldn’t. The important thing is making sure that it’s very difficult to introduce simultaneous bugs in the test and production code that cancel each other out. Unless the tests are tightly coupled with the production code, that comes essentially for free.

Writing the test to reproduce a bug is important because we get to see it go from red to green, which is what gives us confidence. I’ve lost count of how many fake greens I’ve had due to tests that weren’t part of the suite, code that wasn’t even compiled, bugs in the test code, and many other reasons. Making it fail is important. Making changes in a different codebase (the production code) and then the test passing means we’ve actually done something. If at any point things don’t work as they should (red -> green) then we’ve made a mistake somewhere. The fix is wrong, the test is buggy, or our understanding of the problem and what causes it might be completely off.

Reproducing the bug accurately also means that we don’t start with the wrong foot. You might think you know what’s causing the bug, but what better way than to write a failing test? Now, it’s true that one can fix the bug first, write the test later and use the VCS to go back in time and do the red/green dance. But to me that sounds like a lot more work.

Whether you write tests before of after the production code, make sure that at least one test fails without the bugfix. Even if by just changing a number in the production code. I get very suspicious when the test suite is green for a while. Nobody writes correct code that often. I know I don’t.

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Haskell actually does change the way you think

Last year I started trying to learn Haskell. There have been many ups and downs, but my only Haskell project so far is on hold while I work on other things. I’m not sure yet if I’d choose to use Haskell in production. The problems I had (and the time it’s taken so far) writing a simple server make me think twice, but that’s a story for another blog post.

The thing is, the whole reason I decided to learn Haskell were the many reports that it made me you think differently. As much as I like D, learning it was easy and essentially I’m using it as a better C++. There are things I routinely do in D that I wouldn’t have thought of or bother in C++ because they’re easier. But it’s not really changed my brain.

I didn’t think Haskell had either, until I started thinking of solutions to problems I was having in D in Haskell ways. I’m currently working on a build system, and since the configuration language is D, it has to be compiled. So I have interesting problems to solve with regards to what runs when: compile-time or run-time. Next thing I know I’m thinking of lazy evaluation, thunks, and the IO monad. Some things aren’t possible to be evaluated at compile-time in D. So I replaced a value with a function that when run (i.e. at run-time) would produce that value. And (modulo current CTFE limitations)… it works! I’m even thinking of making a wrapper type that composes nicely… (sound familiar?)

So, thanks Haskell. You made my head hurt more than anything I’ve tried learning since Physics, but apparently you’ve made me a better programmer.

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The craziest code I ever wrote

A few years ago at work my buddy Jeff was as usual trying to do something in Go. I can’t remember why, but he wanted to arrange text strings in memory so that they were all contiguous. I said something about C++ and he remarked that the only thing C++11 could do that Go couldn’t would be perhaps to do this work at compile-time. I hadn’t learned D yet (which would have made the task trivial), so I spent the rest of the day writing the monstrosity below for “teh lulz”. It ended up causing my first ever question on Stackoverflow. “Enjoy” the code:

//Arrange strings contiguously in memory at compile-time from string literals.
//All free functions prefixed with "my" to faciliate grepping the symbol tree
//(none of them should show up).

#include <iostream>

using std::size_t;

//wrapper for const char* to "allocate" space for it at compile-time
template<size_t N>
struct String {
    //C arrays can only be initialised with a comma-delimited list
    //of values in curly braces. Good thing the compiler expands
    //parameter packs into comma-delimited lists. Now we just have
    //to get a parameter pack of char into the constructor.
    template<typename... Args>
    constexpr String(Args... args):_str{ args... } { }
    const char _str[N];
};

//takes variadic number of chars, creates String object from it.
//i.e. myMakeStringFromChars('f', 'o', 'o', '') -> String<4>::_str = "foo"
template<typename... Args>
constexpr auto myMakeStringFromChars(Args... args) -> String<sizeof...(Args)> {
    return String<sizeof...(args)>(args...);
}

//This struct is here just because the iteration is going up instead of
//down. The solution was to mix traditional template metaprogramming
//with constexpr to be able to terminate the recursion since the template
//parameter N is needed in order to return the right-sized String<N>.
//This class exists only to dispatch on the recursion being finished or not.
//The default below continues recursion.
template<bool TERMINATE>
struct RecurseOrStop {
    template<size_t N, size_t I, typename... Args>
    static constexpr String<N> recurseOrStop(const char* str, Args... args);
};

//Specialisation to terminate recursion when all characters have been
//stripped from the string and converted to a variadic template parameter pack.
template<>
struct RecurseOrStop<true> {
    template<size_t N, size_t I, typename... Args>
    static constexpr String<N> recurseOrStop(const char* str, Args... args);
};

//Actual function to recurse over the string and turn it into a variadic
//parameter list of characters.
//Named differently to avoid infinite recursion.
template<size_t N, size_t I = 0, typename... Args>
constexpr String<N> myRecurseOrStop(const char* str, Args... args) {
    //template needed after :: since the compiler needs to distinguish
    //between recurseOrStop being a function template with 2 paramaters
    //or an enum being compared to N (recurseOrStop < N)
    return RecurseOrStop<I == N>::template recurseOrStop<N, I>(str, args...);
}

//implementation of the declaration above
//add a character to the end of the parameter pack and recurse to next character.
template<bool TERMINATE>
template<size_t N, size_t I, typename... Args>
constexpr String<N> RecurseOrStop<TERMINATE>::recurseOrStop(const char* str,
                                                            Args... args) {
    return myRecurseOrStop<N, I + 1>(str, args..., str[I]);
}

//implementation of the declaration above
//terminate recursion and construct string from full list of characters.
template<size_t N, size_t I, typename... Args>
constexpr String<N> RecurseOrStop<true>::recurseOrStop(const char* str,
                                                       Args... args) {
    return myMakeStringFromChars(args...);
}

//takes a compile-time static string literal and returns String<N> from it
//this happens by transforming the string literal into a variadic paramater
//pack of char.
//i.e. myMakeString("foo") -> calls myMakeStringFromChars('f', 'o', 'o', '');
template<size_t N>
constexpr String<N> myMakeString(const char (&str)[N]) {
    return myRecurseOrStop<N>(str);
}

//Simple tuple implementation. The only reason std::tuple isn't being used
//is because its only constexpr constructor is the default constructor.
//We need a constexpr constructor to be able to do compile-time shenanigans,
//and it's easier to roll our own tuple than to edit the standard library code.

//use MyTupleLeaf to construct MyTuple and make sure the order in memory
//is the same as the order of the variadic parameter pack passed to MyTuple.
template<typename T>
struct MyTupleLeaf {
    constexpr MyTupleLeaf(T value):_value(value) { }
    T _value;
};

//Use MyTupleLeaf implementation to define MyTuple.
//Won't work if used with 2 String<> objects of the same size but this
//is just a toy implementation anyway. Multiple inheritance guarantees
//data in the same order in memory as the variadic parameters.
template<typename... Args>
struct MyTuple: public MyTupleLeaf<Args>... {
    constexpr MyTuple(Args... args):MyTupleLeaf<Args>(args)... { }
};

//Helper function akin to std::make_tuple. Needed since functions can deduce
//types from parameter values, but classes can't.
template<typename... Args>
constexpr MyTuple<Args...> myMakeTuple(Args... args) {
    return MyTuple<Args...>(args...);
}

//Takes a variadic list of string literals and returns a tuple of String<> objects.
//These will be contiguous in memory. Trailing '' adds 1 to the size of each string.
//i.e. ("foo", "foobar") -> (const char (&arg1)[4], const char (&arg2)[7]) params ->
//                       ->  MyTuple<String<4>, String<7>> return value
template<size_t... Sizes>
constexpr auto myMakeStrings(const char (&...args)[Sizes]) -> MyTuple<String<Sizes>...> {
    //expands into myMakeTuple(myMakeString(arg1), myMakeString(arg2), ...)
    return myMakeTuple(myMakeString(args)...);
}

//Prints tuple of strings
template<typename T> //just to avoid typing the tuple type of the strings param
void printStrings(const T& strings) {
    //No std::get or any other helpers for MyTuple, so intead just cast it to
    //const char* to explore its layout in memory. We could add iterators to
    //myTuple and do "for(auto data: strings)" for ease of use, but the whole
    //point of this exercise is the memory layout and nothing makes that clearer
    //than the ugly cast below.
    const char* const chars = reinterpret_cast<const char*>(&strings);
    std::cout << "Printing strings of total size " << sizeof(strings);
    std::cout << " bytes:\n";
    std::cout << "-------------------------------\n";

    for(size_t i = 0; i < sizeof(strings); ++i) {
        chars[i] == '' ? std::cout << "\n" : std::cout << chars[i];
    }

    std::cout << "-------------------------------\n";
    std::cout << "\n\n";
}

int main() {
    {
        constexpr auto strings = myMakeStrings("foo", "foobar",
                                               "strings at compile time");
        printStrings(strings);
    }

    {
        constexpr auto strings = myMakeStrings("Some more strings",
                                               "just to show Jeff to not try",
                                               "to challenge C++11 again :P",
                                               "with more",
                                               "to show this is variadic");
        printStrings(strings);
    }

    std::cout << "Running 'objdump -t |grep my' should show that none of the\n";
    std::cout << "functions defined in this file (except printStrings()) are in\n";
    std::cout << "the executable. All computations are done by the compiler at\n";
    std::cout << "compile-time. printStrings() executes at run-time.\n";
}
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Emacs as a Python 3 IDE: at last!

Emacs is my editor of choice, I use it to write nearly everything. I have to write Python at work, which I’m ok with since I’m generally a fan of the language. For that I use ropemacs, (with jedi, flycheck and flake8) which makes it possible to use the rope refactoring library from Emacs. Mostly I use it for “go to definition” but its refactoring features are obviously also super useful.

Now, I like new and shiny tech. I run Arch Linux for a reason. I jumped on the C++11 and C++14 bandwagons as soon as I heard about them. So in the Python version debate, if I have a choice I’d go with Python 3 which has been the default on Arch for a while now anyway.

Imagine my dismay when nothing worked anymore in Python 3. My awesome editing environment gone. Rope has a Python 3 version, but ropemacs and ropemode (which ropemacs depends on) don’t. Sadness.

But wait, open source to the rescue! I forked both ropemode and ropemacs and after some porting and much debugging got to something that works: ropemacs_py3k. Enjoy! I know I will.

Type-safety and time intervals in D and Go

My favourite language is now, by far, D. It’s not just not even close. I’ve also been known
to make my opinion on Go be publicly known as “I really don’t like it.”. My work buddy Jeff
is nuts over Go though, and I try not to hold it against him. We keep arguing for “our”
language and disparaging the other guy’s, and it’s all in good fun.

As part of that banter, he sent me a blog post link on Google Plus about flaky tests and what they
tell you about your code. It’s a good read, and exemplifies some of the real-world
engineering problems that happen when developing software. As I read it though, my eyes
roll a bit when I encounter this bit of code:

var Timeout time.Duration
Timeout = time.Duration(conf("timeout", 100)) * time.Millisecond
cache := memcache.New(Servers...)
cache.Timeout = Timeout * time.Millisecond

The first thing I didn’t like about it is that the multiplication by
time.Millisecond looks like C. It’s not that different from
multiplying by a preprocessor macro, with all that entails. Don’t we
know better now? std::chrono from C++ is ugly, but it’s still better
than this.

Related to the C-ness of it, and much more importantly, the second time the code
multiplies by time.Millisecond is the cause of the bug the blog post is about.
And immediately I think: that shouldn’t compile. Maybe it’s my physicist background,
but multiplying time by a time unit shouldn’t work. Or at least you shouldn’t be
able to pass that value to a function expecting a time unit (instead of time squared).

I immediately wrote some D code to make sure I didn’t embarass myself by stating
that in D that would be a compilation error, and to my joy the following code
didn’t compile:

import std.datetime;
void main() {
    auto time = 2.seconds;
    auto oops = time * 3.seconds;
}
foo.d(5): Error: 'time' is not of arithmetic type, it is a Duration
foo.d(5): Error: 'dur(3L)' is not of arithmetic type, it is a Duration

Multiplying time by a scalar works as expected, though, which is what should happen. I showed him the code and he seemed really interested in it, and also wondered
which design decisions led to the current state of affairs. According to him the Go
team likes type-safety, so it seems odd. He also asked me if it would be a compilation
error in Haskell, to which the answer was obviously, in Barney Stinson style, “please!”.

I have to admit letting out a rather childish “sucks to be you” at Jeff today in
the office and basking in my elevated sense of self-worth and computer language choice.

Go D! Pun half-intended.

Haskell monads for C++ programmers

I’m not going to get into the monad tutorial fallacy. Also, I think this blog about another monad fallacy sums it up nicely: the problem isn’t understanding what monads are, but rather understanding how they can be used. Understanding the monad laws isn’t hard. Understanding how to use the Maybe monad isn’t hard either. But things get tricky pretty fast and there’ s a kind of monads that are similar to each other that took me a while to understand how to use. That is, until I recognised what they actually were: C++ template metaprogramming. I guess it’s the opposite realisation that Bartoz Milewski had.

The analogy is only valid for a few monads. The ones I’ve seen that this applies to are IO, State, and Get from Data.Binary. These are the monads that are referred to as computations, which sounds really abstract, but really functions that return these monads return mini-programs. These mini-programs don’t immediately do anything, they need to be executed first. In IO’s case that’s done by the runtime system, for State the runState does that for you (I’m stretching here – only IO really does anything, even runState is pure).

It’s similar to template metaprogramming in C++: at compile-time the programmer has access to a functional language with no side-effects that returns a function that at runtime (i.e. when executed) actually does something. After that realisation I got a lot better at understanding how and why to use them.

The monad issue doesn’t end there, unfortunately. There are many other monads that aren’t like C++ templates at all. But the ones that are – well, at least you’ll be able to recognise them now.

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Unit-tested, acceptance-tested, type-checked and yet still buggy

So I wrote an MQTT broker once (twice really, but I never really finished the second version). It’s now my go-to way of learning a new computer language. Once Rust finally makes it to version 1.0, I’ll write an MQTT broker in it as well. It’s a problem I know well now, it tackles the hairy problems of networking and concurrency, and it’s small enough to not become a huge time sink. So that’s how I decided to try and learn Haskell.

With the immense paradigm shift that came with learning Haskell came a much longer development time. I’m ok with that, especially given that the code is so much shorter. This time I decided to BDD the whole thing, using Cucumber to drive the acceptance tests and writing unit tests for the rest.

After much toiling and trying to wrap my head around monads (I finally understand them! It only took months of reading multiple different blog posts and using them…), I got a version that compiled and passed all tests. A working MQTT broker! So let’s run Jeff’s benchmark on it to see how it compares with the implementations in other languages and… oops.

The program hangs. I try again with 10 messages and 2 connections. It still hangs. How can this be? I did my due dilligence, didn’t I? Aren’t Haskell programs just supposed to work if they compile? I mean, I had to jump around hoops to write a function to convert character literals into Word8 values so write my tests! After a lot of “printf” debugging (the Haskell debugging experience is not ideal) I find what’s causing the bug, and as always, it seems obvious in hindsight.

Networking is dirty, ugly and variable. Not the kind of thing that lends itself well to being unit-tested. So I cheated a bit. The part that dealt with client connections and whether or not the server should disconnect went into the main loop and wasn’t unit tested at all. I felt bad about it at the time but let it go on one of those “it’s ok, I know what I’m doing” feelings. And as usual, I break my own rules at my own peril.

I had an acceptance test for it, and it passed. It just wasn’t comprehensive enough. The TCP traffic has to be just so to trigger the bug, and it’s actually hard to craft packets that look like real-world usage scenarios. Who wants to have a list of bytes 512 bytes long in their test code, much less several of them?

My conclusion of the error of my ways? Not enough unit-testing. Too much code outside the nice, warm and fuzzy pure core. The feeling I shook off about the dirty networking code not being unit tested? I’m never doing that again. The fix is going to be relatively simple: purify as much of the code as I can so it’s trivially unit-testable and have the “real” code be a thin wrapper over the pure code.

The worst is that was already my belief of how to develop robust software. I just didn’t follow my own advice.

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Computer languages: ordering my favourites

This isn’t even remotely supposed to be based on facts, evidence, benchmarks or anything like that. You could even disagree with what are “scripting languages” or not. All of the below just reflect my personal preferences. In any case, here’s my list of favourite computer languages, divided into two categories: scripting and, err… I guess “not scripting”.

 

My favourite scripting languages, in order:

  1. Python
  2. Ruby
  3. Emacs Lisp
  4. Lua
  5. Powershell
  6. Perl
  7. bash/zsh
  8. m4
  9. Microsoft batch files
  10. Tcl

 

I haven’t written enough Ruby yet to really know. I suspect I’d like it more than Python but at the moment I just don’t have enough experience with it to know its warts. Even I’m surprised there’s something below Perl here but Tcl really is that bad. If you’re wondering where PHP is, well I don’t know because I’ve never written any but from what I’ve seen and heard I’d expect it to be (in my opinion of course) better than Tcl and worse than Perl. I’m surprised how high Perl is given my extreme dislike for it. When I started thinking about it I realised there’s far far worse.

 

My favourite non-scripting languages, in order:

  1. D
  2. C++
  3. Haskell
  4. Common Lisp
  5. Rust
  6. Java
  7. Go
  8. Objective C
  9. C
  10. Pascal
  11. Fortran
  12. Basic / Visual Basic

I’ve never used Scheme, if that explains where Common Lisp is. I’m still learning Haskell so not too sure there. As for Rust, I’ve never written a line of code in it and yet I think I can confidently place it in the list, especially with respect to Go. It might place higher than C++ but I don’t know yet.

 

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Summary of CppCon videos I’ve watched so far

There seemed to be a theme going at DConf 2014. Or a few. Lots of “metaprogramming is great but hard” (including my talk) and also lots of “the GC got in the way somehow”.

After watching a few of the CppCon videos from this year, it seems to me that it also has a few recurrent themes. Quite a few talks mentioned using custom allocators but completely doing away with exceptions and/or templates. It seems these practices are more widespread than I thought. It still troubles me that nobody seems interested in measuring what the effects would be. I hear a lot of “exceptions would slow this down”, but no measurements. “Template bloat” shows up a lot but I don’t understand how the code woudn’t be just as large if doing them by hand, bar pathological instances of generating code for int/uint and the like. Besides, isn’t that premature optimisation anyway? Write it neat, then cut down on memory usage if using too much.

Really interesting talks, I recommend everyone to check it out.

Neves’s Laws of Testing

I got into a heated argument over testing practices at work the other day. That led me to think about the subject quite a bit, and have come up with a few laws of testing based on my experience:

  1. Only mock what’s needed to make the test deterministic and fast; otherwise call the real code
  2. A flaky test is worse than no test at all
  3. Good tests verify behaviour; bad tests verify an implementation

The worst thing about law number 3 is that it’s just stating the commonly known knowledge that coupling is bad and should de reduced, but for some reason some people don’t realise they’re increasing it when they write tests for the implementation.

Law number 1 needs some explaining. Mostly I’m only interested in mocking/stubbing/etc. functions that make a unit test no be “pure”. Usual suspects are file system operations, networking, databases, etc. I’ve seen a few examples that don’t fit the “traditional” examples but make good canditates. One that particularly opened my eyes was using a mock log object to state that error messages were logged when calling a function with certain arguments. Another situation is that in some embedded contexts it might be hard to even get a file to compile outside of a specialised environment. Mocking can make sense there too. Basically, whatever makes testing difficult should be mocked. Or to paraphrase Einstein, “All code should be mocked as little as possible, but not less”.

What are your testing laws? How do you disagree with mine?

 

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