  Context: Computational notebooks are a contemporary style of literate
programming, in which users can communicate and transfer knowledge by
interleaving executable code, output, and prose in a single rich document. A
Domain-Specific Language (DSL) is an artificial software language tailored for
a particular application domain. Usually, DSL users are domain experts that may
not have a software engineering background. As a consequence, they might not be
familiar with Integrated Development Environments (IDEs). Thus, the development
of tools that offer different interfaces for interacting with a DSL is
relevant.
  Inquiry: However, resources available to DSL designers are limited. We would
like to leverage tools used to interact with general purpose languages in the
context of DSLs. Computational notebooks are an example of such tools. Then,
our main question is: What is an efficient and effective method of designing
and implementing notebook interfaces for DSLs? By addressing this question we
might be able to speed up the development of DSL tools, and ease the
interaction between end-users and DSLs.
  Approach: In this paper, we present Bacat\'a, a mechanism for generating
notebook interfaces for DSLs in a language parametric fashion. We designed this
mechanism in a way in which language engineers can reuse as many language
components (e.g., language processors, type checkers, code generators) as
possible.
  Knowledge: Our results show that notebook interfaces generated by Bacat\'a
can be automatically generated with little manual configuration. There are few
considerations and caveats that should be addressed by language engineers that
rely on language design aspects. The creation of a notebook for a DSL with
Bacat\'a becomes a matter of writing the code that wires existing language
components in the Rascal language workbench with the Jupyter platform.
  Grounding: We evaluate Bacat\'a by generating functional computational
notebook interfaces for three different non-trivial DSLs, namely: a small
subset of Halide (a DSL for digital image processing), SweeterJS (an extended
version of JavaScript), and QL (a DSL for questionnaires). Additionally, it is
relevant to generate notebook implementations rather than implementing them
manually. We measured and compared the number of Source Lines of Code (SLOCs)
that we reused from existing implementations of those languages.
  Importance: The adoption of notebooks by novice-programmers and end-users has
made them very popular in several domains such as exploratory programming, data
science, data journalism, and machine learning. Why are they popular? In (data)
science, it is essential to make results reproducible as well as
understandable. However, notebooks are only available for GPLs. This paper
opens up the notebook metaphor for DSLs to improve the end-user experience when
interacting with code and to increase DSLs adoption.
