My C++ Tool Belt

I suspect that every developer has a “tool belt” that he or she uses to be productive. By that I mean a collection of tools, libraries and whatever else helps. With a few exceptions, these tool belts will probably be language specific, or at least platform specific. As my projects updated their compilers and transitioned to C++11 and beyond, my C++ tool belt changed quite a bit. Since things like threading, smart pointers and functional abstractions where added to the standard library, those are now already included by default. Today I wanna write about what is in my modernized C++11 tool belt.

The Standard Library

Ever since the tr1 extensions, the standard library has progressed into becoming truly powerful and exceptional. The smart pointers, containers, algorithms are much more language extensions than “just” a library, and they play perfectly with actual language features, such as lambdas, auto and initializer lists.


fmtlib provides placeholder-based text formatting a la Python’s String.format. There have been a few implementations of this idea over the years, but this is the first where I think that it might just dethrone operator<< overloading for good. It's fast, stable, portable and has a nice API.
I begin to miss this library the moment I need to work on a project that does not have it.
The next best thing is Qt’s QString::arg mechanism, with slightly inferior API, a less inclusive license, and a much bigger dependency.


Logging is a powerful tool, both for software development and maintenance. Chances are you are going to need it at one point. spdlog is my favorite choice for this task. It uses fmtlib internally, which is just another plus point. It’s simple, fast and very nice to use due to reuse of fmtlib’s formatting. I usually just include this in my projects and get the included fmtlib for free.


This one is actually part of the most recent C++17, but since that is not widely available yet (meaning not many projects have adopted it), I’m going to list it explicitly. There are also a few alternative implementations, such as the one in Boost or akrzemi1’s single-header variant.
Unlike many other programming languages, C++ has a relatively high emphasis on value types. While reference types usually have a built-in “not available” state (a.k.a. nullptr, NULL, Nothing or nil), an optional can transport intent much clearer. For value types, however, it’s absolutely mandatory to have an optional type. Otherwise, you just end up wrapping the value in a pointer just to make it optional.
Do not, however, fall into the trap of using optional for error handling. It’s not made for that, and other abstractions, such as expected are much better for that.


There is really only one choice when it comes to build tools, and that’s CMake. It’s got its own bunch of weaknesses, but the goods far outweight the bads. With the target_ functions, it’s actually quite nice and scales really well to bigger projects. The main downside here is that it still does not play nice with some tools, most notably visual studio. CLion and QtCreator fare much better. Then again, CMake enables the use of other tools easily, such as clang-tidy.

A word on Boost

Boost is no longer the must-have it once was. Much of the mandated functionality has already been incorporated into the standard library. It is no longer a requirement for a sane C++ project. On the contrary, boost is notoriously huge and somewhat cumbersome to integrate. Boost is not a library, it is a collection of libraries, therefore you can still decide whether to use Boost on a library by library basis. However, much of that is viral, and using a small part of Boost will easily drag in a few hundreds of other Boost headers. The libraries I tend to include most often are Boost.Utility (for boost::noncopyable) and Boost.Filesystem. The former is obviously easy to do without Boost, especially with = delete; and the latter is a part of the standard library since C++17. I hope to be doing the majority of my projects without it in the future. Boost was a catalyst for most of the C++ progress in recent years. It slowly becoming obsolete, either by being integrated into the standard or it’s idioms no longer being needed, is just a sign of its own success.

My honorable mentions are Qt and the stb single file libraries. What are your go-to tools?

Analyzing iOS crash dumps with Xcode

The best way to analyze a crash in an iOS app is if you can reproduce it directly in the iOS simulator in debug mode or on a local device connected to Xcode. Sometimes you have to analyze a crash that happened on a device that you do not have direct access to. Maybe the crash was discovered by a tester who is located in a remote place. In this case the tester must transfer the crash information to the developer and the developer has to import it in Xcode. The iOS and Xcode functionalities for this workflow are a bit hidden, so that the following step-by-step guide can help.

Finding the crash dumps

iOS stores crash dumps for every crash that occured. You can find them in the Settings app in the deeply nested menu hierarchy under Privacy -> Analytics -> Analytics Data.

There you can select the crash dump. If you tap on a crash dump you can see its contents in a JSON format. You can select this text and send it to the developer. Unfortunately there is no “Select all” option, you have to select it manually. It can be quite long because it contains the stack traces of all the threads of the app.

Importing the crash dump in Xcode

To import the crash dump in Xcode you must save it first in a file with the file name extension “.crash”. Then you open the Devices dialog in Xcode via the Window menu:

To import the crash dump you must have at least one device connected to your Mac, otherwise you will find that you can’t proceed to the next step. It can be any iOS device. Select the device to open the device information panel:

Here you find the “View Device Logs” button to open the following Device Logs dialog:

To import the crash dump into this dialog select the “All Logs” tab and drag & drop the “.crash” file into the panel on the left in the dialog.

Initially the stack traces in the crash dump only contain memory addresses as hexadecimal numbers. To resolve these addresses to human readable symbols of the code you have to “re-symbolicate” the log. This functionality is hidden in the context menu of the crash dump:

Now you’re good to go and you should finally be able to find the cause of the crash.

About API astonishments

Nowadays we developers tend to stand on the shoulders of giants: We put powerful building-blocks from different libraries together to build something worth man-years in hours. Or we fill-in the missing pieces in a framework infrastructure to create a complete application in just a few days.

While it is great to have such tools in the form of application programmer interfaces (API) at your disposal it is hard to build high quality APIs. There are many examples for widely used APIs, good and bad. What does “bad API” mean? It depends on your view point:

Bad API for the API user

For the application programmer a bad API means things like:

  • Simple tasks/use cases are complicated
  • Complex tasks are impossible or require patching
  • Easy to misuse producing bugs

A very simple real life example of such an API is a C++ camera API I had to use in a project. Our users were able to change the area of interest (AOI) of the picture to produce images consisting of only a part of full resolution images. Our application did crash or not work as expected without obvious reasons. It took many hours of debugging to spot the subtle API misuse that could be verified be reading the documentation:

The value of camera.Width.GetMax() changed instead of being constant! The reason is that AOI was meant and not the sensor resolution width. The full resolution width we actually wanted is obtained by calling camera.WidthMax.GetValue(). This kind of naming makes the properties almost undistinguishable and communicates nothing of the implications. Terms like AOI or sensor width or full resolution just do not appear in this part of the API.

Small things like the example above may really hurt productivity and user experience of an API.

Bad API for the API programmer

API programmers can easily produce APIs that are bad for themselves because they take away too much freedom away resulting in:

  • Frequent breaking changes
  • API rewrites
  • Unimplementable features
  • Confusing, not fitting interfaces

Design your interfaces small and focused. Use types in the interface that leave as much freedom as possible without hurting usability (see Iterable vs. Collection vs. List vs. ArrayList for example). Try to build composable and extendable types because adding types or methods is less of a problem than changing them.


Developers should put extra care in interfaces they want to publish for others to use. Once the API is out there breaking it means angry users. Be aware that good API design is hard and necessary for a painless evolution of an API. Consider reading books like “Practical API Design” or “Build APIs You Won’t Hate” if you want to target a wider audience.

Internationalization of a React application with react-intl

For the internationalization of a React application I have recently used the seemingly popular react-intl package by Yahoo.

The basic usage is simple. To resolve a message use the FormattedMessage tag in the render method of a React component:

import {FormattedMessage} from "react-intl";

class Greeting extends React.Component {
  render() {
    return (
        <FormattedMessage id="greeting.message"
            defaultMessage={"Hello, world!"}/>

Injecting the “intl” property

If you have a text in your application that can’t be simply resolved with a FormattedMessage tag, because you need it as a string variable in your code, you have to inject the intl property into your React component and then resolve the message via the formatMessage method on the intl property.

To inject this property you have to wrap the component class via the injectIntl() function and then re-assign the wrapped class to the original class identifier:

import {intlShape, injectIntl} from "react-intl";

class SearchField extends React.Component {
  render() {
    const intl = this.props.intl;
    const placeholder = intl.formatMessage({
        id: "search.field.placeholder",
        defaultMessage: "Search"
    return (<input type="search" name="query"
SearchField.propTypes = {
    intl: intlShape.isRequired
SearchField = injectIntl(SearchField);

Preserving references to components

In one of the components I had captured a reference to a child component with the React ref attribute:

ref={(component) => this.searchInput = component}

After wrapping the parent component class via injectIntl() as described above in order to internationalize it, the internal reference stopped working. It took me a while to figure out how to fix it, since it’s not directly mentioned in the documentation. You have to pass the “withRef: true” option to the injectIntl() call:

SearchForm = injectIntl(SearchForm, {withRef: true});

Here’s a complete example:

import {intlShape, injectIntl} from "react-intl";

class SearchForm extends React.Component {
  render() {
    const intl = this.props.intl;
    const placeholder = intl.formatMessage({
        id: "search.field.placeholder",
        defaultMessage: "Search"
    return (
        <input type="search" name="query"
               ref={(c) => this.searchInput = c}/>
SearchForm.propTypes = {
  intl: intlShape.isRequired
SearchForm = injectIntl(SearchForm,
                        {withRef: true});


Although react-intl appears to be one of the more mature internationalization packages for React, the overall experience isn’t too great. Unfortunately, you have to litter the code of your components with dependency injection boilerplate code, and the documentation is lacking.

Simple build triggers with secured Jenkins CI

The jenkins continuous integration (CI) server provides several ways to trigger builds remotely, for example from a git hook. Things are easy on an open jenkins instance without security enabled. It gets a little more complicated if you like to protect your jenkins build environment.

Git plugin notify commit url

For git there is the “notifyCommitUrl” you can use in combination with the Poll SCM settings:


Note two things regarding this approach:

  1. The url of the source code repository given as a parameter must match the repository url of the jenkins job.
  2. You have to check the Poll SCM setting, but you do not need to provide a schedule

Another drawback is its restriction to git-hosted jobs.

Jenkins remote access api

Then there is the more general and more modern jenkins remote access api, where you may trigger builds regardless of the source code management system you use.
curl -X POST $JENKINS_URL/job/$JOB_NAME/build?token=$TOKEN

It allows even triggering parameterized builds with HTTP POST requests like:

curl -X POST $JENKINS_URL/job/$JOB_NAME/build \
--user USER:TOKEN \
--data-urlencode json='{"parameter": [{"name":"id", "value":"123"}, {"name":"verbosity", "value":"high"}]}'

Both approaches work great as long as your jenkins instance is not secured and everyone can do everything. Such a setting may be fine in your companies intranet but becomes a no-go in more heterogenious environments or with a public jenkins server.

So the way to go is securing jenkins with user accounts and restricted access. If you do not want to supply username/password as part of the url for doing HTTP BASIC auth and create users just for your repository triggers there is another easy option:

Using the Build Authorization Token Root Plugin!

Build authorization token root plugin

The plugin introduces a configuration setting in the Build triggers section to define an authentication token:

It also exposes a url you can access without being logged in to trigger builds just providing the token specified in the job:


Or for parameterized builds something like:



The token root plugin does not need HTTP POST requests but also works fine using HTTP GET. It does neither requires a user account nor the awkward Poll SCM setting. In my opinion it is the most simple and pragmatic choice for build triggering on a secured jenkins instance.

Look at the automated tests to diagnose the project ailments

A cornerstone of modern software development is developer testing. That means that developers are the primary authors of automated test code. In theory, that is a good thing and might look like the quality assurance department is out of work soon. In practice, we as a profession tried for nearly twenty years to install a culture of developer testing in our work and still end up with software projects that feature no automated tests at all (Side note: JUnit 1.0 was released in February of 1998).

What we know about automated tests

One piece of common understanding about developer testing is the test pyramide. Let’s iterate quickly what we know about it. There are different kinds of automated tests and the test pyramide differentiates three of them:

  • Acceptance tests or UI tests are the heaviest type of automated test. They operate on the software from the outside, with the means of a real user and try to assert that real use cases are accomplishable.
  • Integration tests often use several parts of the system in a test scenario that asserts the correct collaboration of the parts. Integration tests may take some time to come to a conclusion and utilize real hardware like network or disks.
  • Unit tests tend to be small and quick and focus on a particular aspect of an “unit” like a class or entity aggregate. Their reach into the system should be short and might be forcefully restricted by employing mocks.

These three types, the A, I and U of automated tests, should come in different numbers. A good rule of thumb is that for every acceptance test, there might be up to one thousand unit tests. If you draw the quantities as areas, they appear in form of a pyramide. A small top of acceptance tests rests on a broader seating of integration tests that relies on a groundwork of many unit tests. A healthy test pyramide looks like this:

Take this picture as an orientation, not as an absolute scale. But be sure to count your different test types from time to time.

Outlining the tests

This is actually one of the first things I do when I get introduced to a new and unknown code base. This happens quite often when I do consulting work for existing development teams. Have a look at the automated tests, determine their type and count their numbers. If it resembles anything close to the test pyramide, you’ve got a chance. If the resulting shape looks different, you might find this blog entry useful:

The Tower

If you have a hard time finding any tests (because there are none) or you find only some half-assed attempts to produce a meaningful automated test suite, you look at a tower project. The tower is rather small in diameter, in the cases of absent tests it is nothing more than a thin vertical line (the “stick”). If you find a solid number of tests for every type, you’ve found a “block” project. Block projects usually don’t have a problem, but a history of test effort migration either from unit to acceptance tests or, more common, in the other direction. If you find a block, you are fine.

The tower, though, is a case of neglect. The project team might have started serious efforts to automated their tests, but got demotivated by intrinsic or extrinsic influences and abandoned the tests soon after their creation. Nobody has looked after them since and the only reason they still pass green is that they didn’t really test anything to begin with or only cover an area of the system that is as finished as it is boring. Topics like user management or utility classes are usually the first and only things that got tests in a tower scenario.

Don’t get me wrong, the tower indicates the absence of tests, but not the absence of willingness to write automated tests, unless the tower is really a stick. A team willing to invest in automated tests may only lack knowledge and coaching about the topic. Be sure to lead them bottom-up (unit tests first), though.

The Egg

If you’ve categorized and counted the tests and couldn’t find many acceptance or unit tests, you’ve found an egg. The egg consists of mostly integration tests that may lean into unit testing territory by asserting smallest bits of functionality here and there (often embedded in an overarching test storyline) or dip their toes into gui-based testing by asserting presentation-specific properties of widget objects. While they provide ample test coverage for the system, they also tie application logic and presentation details together and don’t help to separate domain code from the use cases.

The project team is probably proud of their test coverage and doesn’t see any value in differentiating the automated tests types, because “every test improves the situation”. The blindness to test types is the core problem that may be cured with training and coaching (I’ve found the ATRIP-rules to be particularly effective to distinguish integration and unit tests), but the symptoms, especially the lack of separation of concerns, have to be mitigated soon, too.

One way to start there is to break the tests down into their integration and their unit test parts. You can work from assertion to assertion and ask: is this necessary to ensure the current use case? If not, extract a new unit test focussed on only this one assertion.

As soon as you add a pedestal consisting of unit tests to your egg, you are on your best way to a healthy test pyramide.

The Ice Cream Cone

This is the most fearsome automated test outline in existence, even more dramatic than the stick. Usually, the project team is really enthusiastic about writing tests or at least follow order to do so, but they cannot test parts of the application in isolation. A really tragic case was a complex system that was so entangled with its database, through countless stored procedures that contributed to the application logic, that it was hopeless to think about tests without the database. And because every automated test had to start the whole system including the database, there was really no need to differentiate between application logic and presentation logic. It all became a gordic knot of dependencies that enforced the habit of writing elaborate automated GUI-based tests to test the smallest logic bits deep inside the core. It felt like eating single rice grains with overly long, flimsy wooden chopsticks that would break often.

The ice cream cone is problematic because the project team needs to realize that their effort was mislead and the tests are all telling the bitter truth: the system’s architecture isn’t fit for proper automated tests. It’s not the tests, it’s you (or your architecture)! Nobody wants to hear that and more so, nobody wants to untangle the mess (without the help of a proper safety net consisting of automated tests). Pinning tests are probably helpful in this scenario.

But you need to turn the test pyramide around or the project team will suffocate by the overly costly test tax while increasing technical debt.


Please keep in mind that it’s not a problem in itself that your project doesn’t have a normal test pyramide. It’s great that you have automated tests at all! But your current test type distribution might not be as effective as possible, might be more expensive than necessary and might be not the right automated test setup for your development goals.

What are your stories with automated test setups? Care to share it with us in the comments?

CSS 3D transforms

If you are like me when thinking about 3D in the browser you immediately speak of WebGL. But what most developers forget is that we use simple 3D mechanism in our web sites and applications already: the z-index.
While the z-index in only stacking flat containers above each other. Almost all modern browsers can use CSS to create simple 3D models.
Let’s start with a cuboid.

<div class="container">
  <div id="cuboid">
    <div class="front">1</div>
    <div class="back">2</div>
    <div class="right">3</div>
    <div class="left">4</div>
    <div class="top">5</div>
    <div class="bottom">6</div>

We have 6 sides and for easier recognizing each one each has a number on it. We make them bigger and give them a different background to distinguish them further.

.container {
  width: 300px;
  height: 300px;
  position: relative;
  margin: 0 auto 40px;
  padding-top: 100px;

#cuboid {
  width: 100%;
  height: 100%;
  position: absolute;

#cuboid div {
  display: block;
  position: absolute;
  border: 2px solid black;
  line-height: 196px;
  font-size: 120px;
  font-weight: bold;
  color: white;
  text-align: center;

Until now we didn’t use any 3D transformations. For ordering the sides we rotate and translate each side in its place.

    #cuboid .front  { transform: translateZ(100px); }
    #cuboid .back   { transform: rotateX(-180deg) translateZ(0px); }
    #cuboid .right  { transform: rotateY(90deg) translateZ(150px) translateX(-50px); }
    #cuboid .left   { transform: rotateY(-90deg) translateZ(50px) translateX(50px); }
    #cuboid .top    { transform: rotateX(90deg) translateZ(50px) translateY(50px); }
    #cuboid .bottom { transform: rotateX(-90deg) translateZ(200px) translateY(-50px); }

This brought the back side on top but no 3D visible yet. Further we tell the browser to use 3d on its children and move the scene a bit out.

#cuboid {
  transform-style: preserve-3d;
  transform: translateZ( -100px );

Still we are trapped in flatland. Ah we are looking straight onto the front. So we rotate the scene.

#cuboid {
  transform-style: preserve-3d;
  transform: translateZ( -100px ) rotateX(20deg) rotateY(20deg);

Now we have depth. Something is quite not right. If we remember one thing from our OpenGL days we need another ingredient to make it look 3D: a perspective.

.container {
  perspective: 1200px;

Last but not least we add animation to see it spinning.

#cuboid {
  transform-style: preserve-3d;
  transform: translateZ(-100px) rotateX(20deg) rotateY(20deg);
  animation: spinCuboid 5s infinite ease-out;
@keyframes spinCuboid {
  0% { transform: translateZ(-100px) rotateX(0deg) rotateY(0deg); }
  100% { transform: translateZ(-100px) rotateX(360deg) rotateY(360deg); }