IT systems testing *

^{«Clean Code» and a clean cat}

There is nothing developers enjoy better than arguing about clean code: Dan Abramov, for example, has recently fueled the hype with his blog post, «Goodbye, Clean Code».

However, “clean code” per se doesn’t even have a clear definition. The main book on the subject is Clean Code, where Robert «Uncle Bob» Martin states that there are perhaps as many definitions as there are programmers. But he doesn’t walk away from the fact with a conclusion that there’s no reason to discuss clean code, rather — compare several definitions and highlight general ideas. Therefore he cites the views of several outstanding programmers on what clean code is.

So we have also become interested in what people in 2020 think of clean code. Have the views changed since the publication of the book? Do opinions vary in different IT fields (maybe backend developers perceive the idea of clean code differently from testers)?

This spring, Uncle Bob comes to St. Petersburg to give talks at our three conferences: they are about .NET development, testing and JavaScript. Therefore, we’ve asked speakers from each of those conferences to share their opinion on clean code so we could compare the opinions of the industry experts in 2020.

We've already published the results in Russian, and here's the English version. Since the topic is known to provoke discussions, feel free to give your own definition or argue about those already given!

UPD: When we posted this article, Uncle Bob had our conferences in his schedule. Unfortunately, the situation has changed. We updated this post on March 12, to avoid any misunderstanding.

Talking to people at conferences and in comments to articles, we face the following objection: static analysis reduces the time to detect errors, but takes up programmers' time, which negates the benefits of using it and even slows down the development process. Let's get this objection straightened out and try to show that it's groundless.

The PVS-Studio 7.04 release coincided with the release of the Warnings Next Generation 6.0.0 plugin for Jenkins. Right in this release Warnings NG Plugin added support of the PVS-Studio static code analyzer. This plugin visualizes data related to compiler warnings or other analysis tools in Jenkins. This article will cover in detail how to install and configure this plugin to use it with PVS-Studio, and will describe most of its features.

A developer, who first encountered generating emails, has almost no chance to write an application, that will do it correctly. Around 40% of emails, generated by corporate applications, are violating some form of standard, and due to this, there are problems with delivery and display. There are reasons for this: emails are technically more difficult than the web, and operating emails is regulated by a few hundred standards, as well as an uncountable number of generally accepted (and not as much) practices, whereas the email clients are more varied and unpredictable than browsers. Testing may significantly improve the situation, but materials that are dedicated to testing the email system, are practically non-existent.

Mail.ru regularly interacts with its users by email. In our projects, all the components responsible for generating emails and even individual mailings, are subject to mandatory testing. In this article, we will share our experience (learning from our mistakes).

This article is an authorized translation of the original post. The translation was made with the kind help of the guys from PVS-Studio. Thank you, guys!

What encouraged me to write this article is considerable quantity of materials on static analysis, which recently has been increasingly coming up. Firstly, this is a blog of PVS-Studio, which actively promotes itself on Habr posting reviews of errors, found by their tool in open source projects. PVS-Studio has recently implemented Java support, and, of course, developers from IntelliJ IDEA, whose built-in analyzer is probably the most advanced for Java today, could not stay away.

When reading these reviews, I get a feeling that we are talking about a magic elixir: click the button, and here it is — the list of defects right in front of your eyes. It seems that as analyzers get more advanced, more and more bugs will be found, and products, scanned by these robots, will become better and better without any effort on our part.

Well, but there are no magic elixirs. I would like to talk about what is usually not spoken in posts like «here are things that our robot can find»: what analyzers are not able to do, what's their real part and place in the process of software delivery, and how to implement the analysis properly.


Ratchet (source: Wikipedia).

Ability to write good unit tests is an important feature of any developer. But how to understand that your unit tests are correct? Good unit test is like a good chess game. In our case chessmen are the approaches which we are going to discuss in this post. There is no best chessman in a chess game because everything depends on the positions (and a player). Likewise, in unit testing you don't have to distinguish only one approach. In other words, you should use all approaches together to get the best result. So, if you want to win this game, then welcome under the cut.

It is text version of the presentation 2018-04-25 at Saint-Petersburg Linux User Group. Configuration example locates at https://github.com/ultral/ansible-role-testing

I suppose that that you make configuration management, not bash. It means that you have to test it some how. Have you ever tested ansible roles? How do you do it?

Russian version

Let's imagine that you are developing software and hardware appliance. The appliance consists of custom OS distributive, upscale servers, a lot of business logic, as a result, it has to use real hardware. If you release broken appliance, your users will not be happy. How to do stable releases?

I'd like to share my story how we dealt with it.