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Languages & runtimes

Many factors should influence how TTS engineers make technology selections for their projects. Here, we discuss recommendations on how to select the language used in your projects. This document doesn't offer hard-and-fast rules, focusing instead on several language aspects which should be balanced against each other. Also, we explicitly note that language selection is ultimately the decision (and responsibility) of the project team. The guidance below is meant to assist your team make this decision.

This is a living document. After looking at existing tickets, start an issue or pull request in GitHub to suggest changes to these standards.

Our strongest languages

TTS has historically worked in three primary languages: JavaScript, Python, and Ruby. While we certainly have a healthy smattering of other odds and ends, these three are our primary strength as indicated by data from past projects, engineer skill sets, and billable hours. These languages are generally a safe bet as we know we can support them and be productive with them.

Looking at this same data and applying preferences from the guiding factors below, we also see a second tier of familiarity around Go, Java, and PHP. These languages may be good fits depending on the project at hand. In particular, domain preference and client familiarity may be major reasons to select one of these languages for your project. We feel relatively comfortable supporting these languages, but if all other factors are equal, we encourage selecting from our first tier.

Frequently-used frameworks

The following are used widely in TTS:

Purpose Tool
CSS framework More info
Infrastructure/configuration as code Terraform
Static site generator Jekyll (with the uswds-jekyll theme) or Hugo

Project scope

Perhaps the most important factor to weigh when considering languages is the estimated project scope. If we anticipate a large, long-standing project which will be handed off to our agency partners, we should be conservative in our language selection. These projects warrant our most standard approach, which generally translates to the selection of one of our primary languages. On the other hand, if writing a one-off script or small internal project, we have significantly more latitude to try experimental languages.

Let's consider some examples for reference. As a reminder, in almost all situations, selecting one of our primary languages is a viable option.

  • Scrape GitHub statistics one-shot - R would be a fine solution. Due to the small scope of the project, we get much more value from R than we lose due to risk of unmaintainability, etc.
  • Integration tests for an internal app - Here, we've ratcheted up risk: a team will need to maintain these tests in the future. However, if the team were so inclined, using something like Perl might still be a viable option due to the limited impact of these tests.
  • Agency-centric open data API - Given the public-facing project scope and assuming other factors are equal, we should write this in one of our primary languages. We can be confident we'll do the best job, most efficiently with this approach.


Languages are not all equal. We next consider a handful of factors to weigh when selecting a language for a project. In no particular order:

  • Open source - TTS is a strong proponent of open source; we want our language selection to reflect that. We want open APIs, open source binaries, and community participation.
  • Domain preference - certain problem domains emphasize particular languages. For example, the Cloud Foundry community has a preference for Go, while SOAP-heavy specs are more friendly in Java, and Rails projects promote CoffeeScript. We want to use the tools appropriate for the job.
  • Team familiarity - The more TTS engineers who are comfortable with a language, the safer it is to use. We want our project to be accessible to many, both within TTS and without.
  • Stability - bleeding edge languages are more risky. If the standard API is changing every few months, we aren't going to be able to maintain the project. Try to fathom how this bodes when we're handing this project off.
  • Active ecosystem - some languages are on the rise while others are falling or never gained traction. We will be leaning on the community for support and potentially to hire contractors and vendors. Less active communities make this more difficult.
  • Inclusive communities - TTS promotes welcoming cultures and we want to see that reflected in our language selection. We want to reward language leaders who promote diverse opinions and have open standards processes. We see languages led by cabals as risks.
  • Library support - we don't want to reinvent the wheel (or the database adapter). Languages with a thriving library ecosystem will be easier and most cost effective to work with.
  • Hand-off considerations - is our agency partner comfortable with a particular framework? If they already know how to deploy XYZ apps (and hire XYZ developers), it behooves us to write an XYZ app.

Other impact

By setting forth the above, we can imagine potential impact on our hiring, project selection, and larger processes. The progression is logical and we may want to use this document as evidence when deciding how to iterate those systems. Concrete details are outside the scope of this document, however, and we anticipate (and proclaim) no immediate changes.

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