Over the years of working on TheDarkMod game, we have amassed 1K lines of natvis code, 95 type definitions in total. Natvis has become an inherent part of good debugging experience, but it is available only on Windows. This story is about my attempt to extend this experience to the Linux side.

This multipart article explains how to reimplement most of the inherent natvis features on top of GDB pretty printers, provides a declarative approach to pretty printers similar to how natvis works, gives a solid deep-printing implementation similar to that of MSVC, and discusses the major issues of GDB value inspection system.

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It is well-known that Debug build in Visual C++ is very slow compared to Release build, with the typical ratio about 10-20 times. Various reasons behind it are often stated, and some people even believe that it is inevitable because it is caused by lack of compiler optimizations.

If some issue happens only on a large dataset or in a real-time application, and it cannot be extracted into a smaller test case, then developer is forced to debug Release build. Which is rather painful experience, because debugger has problems showing control flow, values of local variables, sometimes even currently executed function, since all these concepts are messed up by optimizations. Ironically, the more optimizer-friendly your code is, the worse would be your experience of debugging Release build =)

Luckily, Visual C++ provides a lot of settings for tuning speed-vs-comfort ratio. It is entirely possible to create a "Fast Debug" configuration which works only a few times slower than Release yet is pretty easy to debug due to lack of optimizations.

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It is known that code bloat significantly increases build times in C++. The question is: how can we quantify code bloat? How can we see things which are compiled too many times or the things which generate too much object code? How can we know which places would benefit most from improvement efforts?

This article is about an excellent tool called SymbolSort, which produces useful code bloat statistics in a project of any size. In particular, the article describes a few improvements which I have implemented recently to provide more useful analysis.

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