Unit Testing with Erlang’s Common Test Framework

本文介绍如何使用Erlang的Common Test框架进行单元测试。通过详细的目录结构设置、测试套件编写示例及Makefile配置,展示了从零开始搭建Erlang项目的测试流程。
原文地址: http://streamhacker.wordpress.com/2008/11/26/unit-testing-with-erlangs-common-test-framework/

Unit Testing with Erlang’s Common Test Framework

November 26, 2008 at 10:57 am (erlang) (common_test, eunit, make, otp, testing)

One of the first things people look for when getting started with Erlang is a unit testing framework, and EUnit tends to be the framework of choice. But I always had trouble getting EUnit to play nice with my code since it does parse transforms, which screws up the handling of include files and record definitions. And because Erlang has pattern matching, there’s really no reason for assert macros. So I looked around for alternatives and found that a testing framework called common_test has been included since Erlang/OTP-R12B. common_test (and test_server), are much more heavy duty than EUnit, but don’t let that scare you away. Once you’ve set everything up, writing and running unit tests is quite painless.
Directory Setup

I’m going to assume an OTP compliant directory setup, specifically:

1. a top level directory we’ll call project/
2. a lib/ directory containing your applications at project/lib/
3. application directories inside lib/, such as project/lib/app1/
4. code files are in app1/src/ and beam files are in app1/ebin/

So we end up with a directory structure like this:

project/
lib/
app1/
src/
ebin/

Test Suites

Inside the app1/ directory, create a directory called test/. This is where your test suites will go. Generally, you’ll have 1 test suite per code module, so if you have app1/src/module1.erl, then you’ll create app1/test/module1_SUITE.erl for all your module1 unit tests. Each test suite should look something like this: (unfortunately, wordpress doesn’t do syntax highlighting for erlang, so it looks kinda crappy)

-module(module1_SUITE).

% easier than exporting by name
-compile(export_all).

% required for common_test to work
-include("ct.hrl").

%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% common test callbacks %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Specify a list of all unit test functions
all() -> [test1, test2].

% required, but can just return Config. this is a suite level setup function.
init_per_suite(Config) ->
% do custom per suite setup here
Config.

% required, but can just return Config. this is a suite level tear down function.
end_per_suite(Config) ->
% do custom per suite cleanup here
Config.

% optional, can do function level setup for all functions,
% or for individual functions by matching on TestCase.
init_per_testcase(TestCase, Config) ->
% do custom test case setup here
Config.

% optional, can do function level tear down for all functions,
% or for individual functions by matching on TestCase.
end_per_testcase(TestCase, Config) ->
% do custom test case cleanup here
Config.

%%%%%%%%%%%%%%%%
%% test cases %%
%%%%%%%%%%%%%%%%

test1(Config) ->
% write standard erlang code to test whatever you want
% use pattern matching to specify expected return values
ok.

test2(Config) -> ok.

Test Specification

Now the we have a test suite at project/app1/test/module1_SUITE.erl, we can make a test specification so common_test knows where to find the test suites, and which suites to run. Something I found out that hard way is that common_test requires absolute paths in its test specifications. So instead of creating a file called test.spec, we’ll create a file called test.spec.in, and use make to generate the test.spec file with absolute paths.
test.spec.in

{logdir, "@PATH@/log"}.
{alias, app1, "@PATH@/lib/app1"}.
{suites, app1, [module1_SUITE]}.

Makefile

src:
erl -pa lib/*/ebin -make

test.spec: test.spec.in
cat test.spec.in | sed -e "s,@PATH@,$(PWD)," > $(PWD)/test.spec

test: test.spec src
run_test -pa $(PWD)/lib/*/ebin -spec test.spec

Running the Tests

As you can see above, I also use the Makefile for running the tests with the command make test. For this command to work, run_test must be installed in your PATH. To do so, you need to run /usr/lib/erlang/lib/common_test-VERSION/install.sh (where VERSION is whatever version number you currently have). See the common_test installation instructions for more information. I’m also assuming you have an Emakefile for compiling the code in lib/app1/src/ with the make src command.
Final Thoughts

So there you have it, an example test suite, a test specification, and a Makefile for running the tests. The final file and directory structure should look something like this:

project/
Emakefile
Makefile
test.spec.in
lib/
app1/
src/
module1.erl
ebin/
test/
module1_SUITE.erl

Now all you need to do is write your unit tests in the form of test suites and add those suites to test.spec.in. There’s a lot more you can get out of common_test, such as code coverage analysis, HTML logging, and large scale testing. I’ll be covering some of those topics in the future, but for now I’ll end with some parting thoughts from the Common Test User’s Guide:

It’s not possible to prove that a program is correct by testing. On the contrary, it has been formally proven that it is impossible to prove programs in general by testing.

There are many kinds of test suites. Some concentrate on calling every function or command… Some other do the same, but uses all kinds of illegal parameters.

Aim for finding bugs. Write whatever test that has the highest probability of finding a bug, now or in the future. Concentrate more on the critical parts. Bugs in critical subsystems are a lot more expensive than others.

Aim for functionality testing rather than implementation details. Implementation details change quite often, and the test suites should be long lived.

Aim for testing everything once, no less, no more
根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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