Taken from the SHARK website:
SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides methods for linear and nonlinear optimization, in particular evolutionary and gradient-based algorithms, kernel-based learning algorithms and neural networks, and various other machine learning techniques. SHARK serves as a toolbox to support real world applications as well as research in different domains of computational intelligence and machine learning. The sources are compatible with the following platforms: Windows, Solaris, MacOS X, and Linux.
The library has been in active development for over 10 years now and is in use by scientists all over the world. Last year, we, the core SHARK developers, decided that a rewrite of the library is necessary to support future use cases and provide a solid platform for users and contributors, alike. Our goals were simple:
- Unify and simplify the library structure.
- Rely on established components wherever feasible.
- Documentation, documentation and again, documentation
- Focus on quality.
In this post, I would like to dive a little deeper into the topic of quality and the processes that we established to ensure a constant and high level of quality. We decided to address quality both from a technical (read: testable) and from an API point of view.
In terms of API quality, we want the programming interface to be consistent, convenient to use and easy to extend. In equivalence to the user experience, we want potential developers to experience a welcoming and friendly environment. As we are a geographically distributed team of developers and scientists, we decided to go for a pre-commit code review approach implemented with the help of ReviewBoard. Despite initial concerns on behalf of the developers, the review process proved to be one of the most useful tools while rewriting the library with developers starting to like the final “Ship It” quickly.
In terms of “technical” quality, we decided to go for continous integration of all (reviewed) commits to the rewrite branch for all of our supported platforms. With the help of Jenkins and a bunch of virtual machines, we finally realized our idea of continous integration testing to prevent from regressions. Our unit test suite is implemented with the unit testing framework provided by boost. Test execution is handled by CTest. Static and dynamic analysis of the library is carried out with the help of cppcheck and valgrind, respectively. Code coverage metrics are calculated with the help of gcov. Finally, we are integrating all of the testing results in the job-specific views of our Jenkins instance, thereby providing developers a single source of information on the state of the library.