Software Development

Software Development#

Daniel Schwen (@dschwen), Jon Guyer (@guyer), Trevor Keller (@tkphd)

People don’t usually arrive at phase-field methods from a software development background. This page recommends some approaches to reduce errors and wasted time before reaching a usable state – including the installation process.

  • Start small and ensure quality before getting bigger

  • Consider whether to invoke an existing library or implementation to save time, or write it yourself to gain a deeper understanding or implement non-standard features

Follow good coding practices#

The choice of the programming language requires a multitude of considerations

  • Ease of development, familiarity of the language by the development team

  • Target architecture and operating systems, portability

  • Use of parallel computing infrastructures, MPI, GPU support

  • Performance of the resulting code, compiled, interpreted, hybrid approaches

Commenting code and design#

Comment code generously. Whether it is for other developers working on the code or for yourself a couple of years (months?) in the future.

“Code is more often read than written.”

Documentation can be roughly split into API documentation and inline code documentation.

  • API documentation facilitates reuse of the code.

    • Doxygen is the de facto standard for C++ API documentation, but supports many other languages, such as C, C#, Java, and Python.

    • Sphinx is the de facto standard for Python API documentation.

    • ReadTheDocs is a widely used hosting site for software documentation.

  • Inline code comments improve the readability and should capture the intent of the documented code (which can be very valuable for debugging purposes).

    • Choose variable names that reflect their function, and don’t be afraid of long names.

    • Use longer comments and docstrings to provide context for larger blocks of code.

API documentation should be augmented by design documents that offer a high level overview over a code, library, or framework. Design documentation describes the way different pars of the code fit together and their roles in the overall project.

End-user documentation#

Key to successful software projects with broad adoption is good end-user documentation. To ease the learning curve for new users, this documentation should contain

  • Installation

    • Hardware requirements and software dependencies

    • How to build

    • How to install

    • HPC considerations

    • “Run the docker image” is not a (complete) installation instruction

  • Introductory tutorials

  • Examples

  • Theory docs

  • Reference documentation

Version control and metadata#

To facilitate contributions to a code, version control is indispensable. Version control keeps track of all changes to the code and keeps every team member working on the right version. Version control systems enable the use of code review workflows (such as the pull request workflow on GitHub. Code review in turn allows for input from multiple developers and for productive feedback and improvement cycles leading to a higher code quality. Open version control platforms also invite outside contributions and user engagement, leading to project growth.

On open platforms, it is important to add and maintain metadata, such as the code license. Authorship is tracked through the version control system.

License considerations#

[Largely not unique to phase field, or even to scientific computing, so not a lot to say here, but should reference good guidance]

  • If you do not spell out your license, potential users must assume you are granting no rights to copy or reuse it.

  • If your institution stipulates a license, use it.

  • If choosing your own, understand the implications of the license you apply:

    • Some licenses may be incompatible with institutional requirements

    • Some licenses, e.g., GPL, may be an obstacle to commercial adoption

  • Don’t try to write your own!

  • http://choosealicense.org

  • https://tldrlegal.com

Continuous integration and testing#

Strive for a large test coverage of your code. Ideally, tests should be added to your project that exercise every single line of written code. The utility of a thorough test coverage is twofold:

  1. It protects against regressions, i.e., unintended changes in code behavior. This in turn liberates developers to refactor code as needed to improve on code design and code quality.

  2. Tests can be used by the end-users to verify that they have built a fully working version of the code.

Tests can come in the form of small problems (e.g. mini phase field problems) or unit tests. Unit tests do not build a complete simulation, but allow targeted and fast testing of select components of a code. In practice, a mix of execution of the full code and unit tests will be required to achieve comprehensive coverage.

Use a continuous integration (CI) infrastructure. CI systems can connect to version control systems and trigger automatic builds and execution of test suites in your development workflow. For example, testing should occur for every pull request that is made to a code, to ensure that the software is never put into a broken state by incorporating new changes.

The optimal CI infrastructure builds and tests the project with all explicitly supported compilers on all explicitly supported operating systems. In practice, the resulting matrix can be very large in particular when it comes to multiple versions of supported compilers/toolchains, which can be mitigated by declaring and testing a minimal version for each compiler in addition to a current version.

Code verification#

While regression tests only guarantee to maintain the status quo, they do not guarantee the correctness of the simulation results. Code verification is required to ensure the code does what it is designed to do. Several verification approaches exist.

  1. Comparison to analytic solutions. This is often only achievable for the simplest problems (after all most simulation software is developed precisely to numerically solve problems that have no analytical solutions).

  2. Method of manufactured solutions (also known as forced solutions). Here, a user defined solution to an equation system is forced by applying boundary conditions and source terms derived from inserting the user defined solution into the equation system.

  3. Scaling laws. Known scaling laws, such as coarsening rate over time or convergence rate as a function of domain discretization, can be tested to discover implementation errors.

TODO#

  • Am I disseminating this software?