IT conferences and meetings on programming languages see a growing number of speakers talking about static code analysis. Although this field is quite specific, there is still a number of interesting discussions to be found here to help programmers understand the methods, ways of use, and specifics of static code analysis. In this article, we have collected a number of videos on static analysis whose easy style of presentation makes them useful and interesting to a wide audience of both skilled and novice programmers.
What is Static Analysis?
Author: Matt Might
Static analyzers allow programmers to bound and predict the behavior of software without running it. Once used exclusively for program optimization, they have rapidly risen in prominence for areas like software security and automatic parallelization. The author takes you on a tour of the landscape of static analysis through the lens of abstract interpretation.
Static code analysis used for code clean up
Author: PVS-Studio team
The report gives information about ways to detect bugs, methodology of static analysis, correct and incorrect use of analysis tools. The author also provides myths about static analysis that may lead to erroneous understanding among the developers. The presentation shows errors in Open Source projects, detected by such tools as ReSharper, PVS-Studio, Visual Studio SCA.
Static Code Analysis: Scan All Your Code For Bugs
Author: Jared DeMott
The author discusses static code analysis and how it is used in bug elimination. The talk covers a discussion of pattern matching, procedural, data flow, and statistical analysis, and also includes examples of common software vulnerabilities such as memory corruption, buffer overflow and over reads, script injection, XSS and CSRF, command injection, and misconfigurations.
More read at the link - https://www.viva64.com/en/b/0501/
In this article we'll look at the main features of SonarQube - a platform for continuous analysis and measurement of code quality, and we'll also discuss advantages of the methods for code quality evaluation based on the SonarQube metrics.
SonarQube is an open source platform, designed for continuous analysis and measurement of code quality. SonarQube provides the following capabilities:
One of the main problems with C++ is having a huge number of constructions whose behavior is undefined, or is just unexpected for a programmer. We often come across them when using our static analyzer on various projects. But, as we all know, the best thing is to detect errors at the compilation stage. Let's see which techniques in modern C++ help writing not only simple and clear code, but make it safer and more reliable.
What is Modern C++?
The term Modern C++ became very popular after the release of C++11. What does it mean? First of all, Modern C++ is a set of patterns and idioms that are designed to eliminate the downsides of good old "C with classes", that so many C++ programmers are used to, especially if they started programming in C. C++11 looks way more concise and understandable, which is very important.
Nowadays a lot of projects are opening their source code and letting those who are interested in the development of it edit the code. OpenJDK is no exception, programmers PVS-Studio have found a lot of interesting errors that are worth paying attention to.
OpenJDK (Open Java Development Kit) - a project for the creation and implementation of Java (Java SE) platform, which is now free and open source. The project was started in 2006, by the Sun company. The project uses multiple languages- C, C++, and Java. We are interested in the source code written in C and C++. Let's take the 9th version of OpenJDK. The code of this implementation of Java platform is available at the Mercurial repository.
During verification, the analyzer found different errors in the project including: copy-paste, bugs in the operation precedence, errors in logical expressions and in pointer handling and other bugs, which are described in detail in this article.
It's always amusing to check a project which is used and maintained by a large number of people. The better and more accurate the code is, the more safely and effectively the program will work. Those bugs we found, are another proof of the usefulness of an analyzer, as it allows the detection of such errors which would otherwise be hard to detect doing simple code review.
Here is a small e-Book for your attention: The Ultimate Question of Programming, Refactoring, and Everything. This book is intended for C/C++ programmers, but it could be of interest for developers using other languages as well.
What makes the book peculiar is the descriptions of real, not theoretical cases at the base of it. Each chapter starts with a code fragment taken from a real application, and then the author gives various tips of how this bug could be avoided. The questions touched upon in this book can help the readers improve the personal coding style and the coding standards used in the team.
CppCat is a static code analyzer integrating into the Visual Studio 2010-2013 environment. The analyzer is designed for regular use and allows detecting a large number of various errors and typos in programs written in C and C++. For the purpose of popularizing it, we've decided to launch a student-support program granting free licenses to every higher school student who will contact and ask us about that. You just need to send us a photo of your student card or transcript.
The authors of the PVS-Studio analyzer invite you to test your attentiveness.
Code analyzers never get tired and can find errors a human's eye cannot easily notice. We have picked a few code fragments with errors revealed by PVS-Studio, all the fragments taken from well-known open-source projects.
We invite you to take part in a competition against code analyzers to test your agility by trying to find the errors by yourself. You will be offered 15 randomly selected tasks. Every correct answer earns you one score if you give it within 60 seconds. The code fragments are short and 60 seconds is a fair limit.
Let's examine a couple of examples with errors for you to understand how to give the answer.
As you know, our main activity is development of the code analyzers PVS-Studio and CppCat. Although we have been doing this for a long time now and - as we believe - quite successfully, an unusual idea struck us recently. You see, we do not use our own tools in exactly the same way our customers do. Well, we analyze the code of PVS-Studio by PVS-Studio of course, but, honestly, the PVS-Studio project is far from large. Also, the manner of working with PVS-Studio's code is different from that of working with Chromium's or LLVM's code, for example.
We felt like putting ourselves in our customers' shoes to see how our tool is used in long-term projects. You see, project checks we regularly do and report about in our numerous articles are done just the way we would never want our analyzer to be used. Running the tool on a project once, fixing a bunch of bugs, and repeating it all again just one year later is totally incorrect. The routine of coding implies that the analyzer ought to be used regularly - daily.
OK, what's the purpose of all that talk? Our theoretical wishes about trying ourselves in third-party projects have coincided with practical opportunities we started to be offered not so long ago. Last year we decided to allocate a separate team in our company to take up - ugh! - outsourcing; that is, take part in third-party projects as a developer team. Moreover, we were interested in long-term and rather large projects, i.e. requiring not less than 2-3 developers and not less than 6 months of development. We had two goals to accomplish:
- try an alternative kind of business (custom development as opposed to own product development);
- see with our own eyes how PVS-Studio is used in long-term projects.
In this article I'm going to discuss a problem few people think of. Computer simulation of various processes becomes more and more widespread. This technology is wonderful because it allows us to save time and materials which would be otherwise spent on senseless chemical, biological, physical and other kinds of experiments. A computer simulation model of a wing section flow may help significantly reduce the number of prototypes to be tested in a real wind tunnel. Numerical experiments are given more and more trust nowadays. However, dazzled by the triumph of computer simulation, nobody notices the problem of software complexity growth behind it. People treat computer and computer programs just as a means to obtain necessary results. I'm worried that very few know and care about the fact that software size growth leads to a non-linear growth of the number of software bugs. It's dangerous to exploit a computer treating it just as a big calculator. So, that's what I think - I need to share this idea with other people.
I'm currently experiencing a strong cognitive dissonance, and it won't let me go. You see, I visit various programmers' forums and see topics where people discuss noble ideas about how to write super-reliable classes; somebody tells he has his project built with the switches -Wall -Wextra -pedantic -Weffc++, and so on. But, God, where are all these scientific and technological achievements? Why do I come across most silly mistakes again and again? Perhaps something is wrong with me?