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Anyone who programs microcontrollers probably knows about FreeRTOS, or at least heard of this operating system. Amazon developers decided to enhance the abilities of this operating system to work with AWS Internet of Things services. This is how Amazon FreeRTOS appeared. We, developers of the PVS-Studio static code analyzer, were asked by mail and in comments to check these projects. Well, now get what you asked for. Keep reading to find out what came out of it.

Kubernetes has already become a de-facto standard for running stateless applications, mainly because it can reduce time-to-market for new features. Launching stateful applications, such as databases or stateful microservices, is still a complex task, but companies have to meet the competition and maintain a high delivery rate. So they create a demand for such solutions.

We want to introduce our solution for launching stateful Tarantool Cartridge clusters: Tarantool Kubernetes Operator, more under the cut.

This article reviews the results of a second check of the Orchard project with the PVS-Studio static analyzer. Orchard is an open-source content manager system delivered as part of the ASP.NET Open Source Gallery under the non-profit Outercurve Foundation. Today's check is especially interesting because both the project and the analyzer have come a long way since the first check, and this time we'll be looking at new diagnostic messages and some nice bugs.

In 2017, we won the competition for the development of the transaction core for Alfa-Bank's investment business and started working at once. (Vladimir Drynkin, Development Team Lead for Alfa-Bank's Investment Business Transaction Core, spoke about the investment business core at HighLoad++ 2018.) This system was supposed to aggregate transaction data in different formats from various sources, unify the data, save it, and provide access to it.

In the process of development, the system evolved and extended its functions. At some point, we realized that we created something much more than just application software designed for a well-defined scope of tasks: we created a system for building distributed applications with persistent storage. Our experience served as a basis for the new product, Tarantool Data Grid (TDG).

I want to talk about TDG architecture and the solutions that we worked out during the development. I will introduce the basic functions and show how our product could become the basis for building turnkey solutions.

On October 7, 2019, Apple released a new version of its Mac operating system, macOS Catalina. Version 10.15 contains many changes and improvements. One of the significant is the complete phasing out of 32-bit applications. As a developer of such macOS apps, what can you do? That's right, port the app to the 64-bit platform. Will the application work properly from the first attempt? Perhaps, it's possible. Depends on the complexity and amount of the code. But most likely, developers will face a lot of non-obvious errors, which can previously detected using PVS-Studio.

Hello, everyone. Today we have another Microsoft project on the check. By the title of this article, you can guess that this time developers didn't «please» us with a large number of errors. We hope the project's authors won't be offended by the title. After all, a small number of errors is great, isn't it? However, we still managed to find something intriguing in the Azure PowerShell code. We suggest getting to know the features of this project and checking out errors, found using the PVS-Studio C# analyzer.

Telegram Open Network (TON) is a platform by the same team that developed the Telegram messenger. In addition to the blockchain, TON provides a large set of services. The developers recently made the platform's code, which is written in C++, publicly available and uploaded it to GitHub. We decided to check the project before its official release.

With the advent of mobile phones with high-quality cameras, we started making more and more pictures and videos of bright and memorable moments in our lives. Many of us have photo archives that extend back over decades and comprise thousands of pictures which makes them increasingly difficult to navigate through. Just remember how long it took to find a picture of interest just a few years ago.

One of Mail.ru Cloud’s objectives is to provide the handiest means for accessing and searching your own photo and video archives. For this purpose, we at Mail.ru Computer Vision Team have created and implemented systems for smart image processing: search by object, by scene, by face, etc. Another spectacular technology is landmark recognition. Today, I am going to tell you how we made this a reality using Deep Learning.

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.

This article is the story how we once decided to improve our internal SelfTester tool that we apply to test the quality of the PVS-Studio analyzer. The improvement was simple and seemed to be useful, but got us into some troubles. Later it turned out that we'd better gave up the idea.

This article focuses on the specifics of checking Unreal Engine projects with the PVS-Studio static analyser on the Windows operating system: how to install the analyser, check a project, where and how to view an error report.

It is a new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.

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If an instance of a class doesn’t have an attribute with the given name, it tries to access the class attribute with the same name.

>>> class A:
...     x = 2
...
>>> A.x
2
>>> A().x
2

This report explains how the outage of a single AS can affect the connectivity of the impacted region with the rest of the world, especially when it is the dominant ISP on the market. Internet connectivity at the network level is driven by interaction between autonomous systems (AS’s). As the number of alternate routes between AS’s increases, so goes the fault-resistance and stability of the internet across the network. Although some paths inevitably become more important than others, establishing as many alternate routes as possible is the only viable way to ensure an adequately robust system.

The global connectivity of any AS, regardless of whether it is a minor provider or an international giant, depends on the quantity and quality of its paths to Tier-1 ISPs. Usually, Tier-1 implies an international company offering global IP transit service over connections to other Tier-1 providers. But there is no guarantee that such connectivity will be maintained. Only the market can motivate them to peer with other Tier-1’s to deliver the highest quality service. Is that enough? We explore this question in the IPv6 section below. For many ISPs at all levels, losing connection to just one Tier-1 peer would likely render them unreachable in some parts of the world.

Measuring Internet Reliability

Let’s examine a case where an AS experiences significant network degradation. We want to answer the following question: “How many AS’s in the region would lose connectivity with Tier-1 operators and their global availability along with it?”

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 will tell you how to launch analysis of an embedded project and how to work with the analyzer's report. The PVS-Studio analyzer supports a number of compilers for embedded systems. The current version allows checking projects built with one of the following compilers under Window, Linux, and macOS:

• IAR Embedded Workbench
• Keil Embedded Development Tools for Arm
• TI ARM Code Generation Tools
• GNU Embedded Toolchain

Over the years, the PVS-Studio team has been developing the same-name static analyzer. At this point the analyzer represents a complex software solution, which provides the analysis of such programming languages, as C, C++, C# and Java on Windows, Linux and macOS platforms. Just recently the Java language joined the ranks of supported languages. The PVS-Studio analyzer has proved itself as a reliable tool among C++ and C# developers in quite some time, whereas for Java audience PVS-Studio is still a newcomer. Many haven't even heard of the analyzer, and those who had, aren't quite familiar with all its abilities. So in this article, I'd like to introduce PVS-Studio Java to you, talk about the ways to start it and its abilities.

Nowadays, talking to conversational agents is becoming a daily routine, and it is crucial for dialogue systems to generate responses as human-like as possible. As one of the main aspects, primary attention should be given to providing emotionally aware responses to users. In this article, we are going to describe the recurrent neural network architecture for emotion detection in textual conversations, that participated in SemEval-2019 Task 3 “EmoContext”, that is, an annual workshop on semantic evaluation. The task objective is to classify emotion (i.e. happy, sad, angry, and others) in a 3-turn conversational data set.

Nowadays there is no need to implement the physics of objects from scratch for game development because there are a lot of libraries for this purpose. Bullet was actively used in many AAA games, virtual reality projects, various simulations and machine learning. And it is still used, being, for example, one of the Red Dead Redemption and Red Dead Redemption 2 engines. So why not check the Bullet with PVS-Studio to see what errors static analysis can detect in such a large-scale physics simulation project.

Hi everybody! I’m a research engineer at the Mail.ru Group computer vision team. In this article, I’m going to tell a story of how we’ve created AI-based photo restoration project for old military photos. What is «photo restoration»? It consists of three steps:

• we find all the image defects: fractures, scuffs, holes;
  
• we inpaint the discovered defects, based on the pixel values around them;
  
• we colorize the image.
  

Further, I’ll describe every step of photo restoration and tell you how we got our data, what nets we trained, what we accomplished, and what mistakes we made.