In the first part of the article, I will enumerate lots of UNIX cheap and dirty hacks, and other various drawbacks. In the second part, we’ll talk about the UNIX philosophy. This article was written hastily, and I don’t want to further improve it. You’re lucky I wrote it. Therefore, I may provide some facts without source links. Dirty hacks in UNIX started to arise when UNIX was released, and it was long before Windows came to the scene, I guess there wasn’t even Microsoft DOS at the time (I guess and I don’t bother to check, so check it yourself).
About two months ago I wrote an article about the analysis of GCC using PVS-Studio. The idea of the article was as follows: GCC warnings are great, but they’re not enough. It is necessary to use specialized tools for code analysis, for example, PVS-Studio. As proof of my words I showed errors that PVS-Studio was able to find the GCC code. A number of readers have noticed that the quality of the GCC code, and its diagnosis, aren’t really great; while Clang compiler is up to date, of high quality, and fresh.
I regularly check various open-source projects to demonstrate the abilities of the PVS-Studio static code analyzer (C, C++, C#). Now it is time for the GCC compiler to get checked. Unquestionably, GCC is a very qualitative and well-tested project, that’s why it’s already a great achievement for a tool to find any errors in it. Fortunately, PVS-Studio coped with this task. No one is immune to typos or carelessness. This is why the PVS-Studio can become an additional line of defense for you, on the front of the endless war against bugs.
When discussing static analysis tools for C# projects, programmers will often deny the necessity of static analysis arguing that most errors can be caught through unit testing. So, I decided to find out how well one of the most popular unit-testing frameworks, NUnit, was tested and see if our analyzer could find anything of interest there. Introduction NUnit is a popular unit-testing library for .NET projects ported from Java to C#.
In short, the crowdfunding campaignto launch the new physics discipline of Complex Thermostatistics ends this weekend, and we could really use your support to get us over our reach goal! Help us grow a community of researchers and science-lovers who are passionate about understanding complex systems. We will be working on an adaptation of Thermostatistics together for this purpose! Your contributions will provide a year-long runway for a collaborative infrastructure as well as educational materials for this emerging field.
As tasks share time on one processor kernel, I had to deal with multitasking management. Or rather pseudo-multitasking as tasks share time at one processor kernel. At first I’ll try to tell about the types of multitasking (cooperative and preemptive). Then I’ll move on to scheduling principles for the preemptive multitasking. The article is optimized for first-readers who want to understand how multitasking operates at the kernel level. But as everything will be accompanied with examples which can be compiled and run, it may interest the ones who are familiar with the theory, but have never “tasted” the scheduler.
The first version of LibreS3 project has been announced. LibreS3 is a robust open source implementation of the Amazon S3 service, supporting a subset of the S3 REST API. It’s compatible with Amazon S3 client libraries and built on their basis tools, such as python-boto, s3cmd and DragonDisk. The source codes of LibreS3 are written in С and OCaml languages, the code is allocated under the GPLv2 license (libraries under LGPL).