\Citet{mcdonell_embedded_2022} extends on this idea, resulting in a very similar but different solution to ours.
They used the technique that \citeauthor{atkey_unembedding_2009} showed and applied it to deep embedding using the concrete syntax of the host language.
The scaffolding---e.g.\ generating the pairs, sums and injections---for embedding is automated using generics but the required pattern synonyms are generated using \gls{TH}.
-The key difference to our approach is that we specialise the implementation for each of the backends instead of providing a general implementation of data type handling operations.
+The key difference to our approach is that we specialise the implementation for each of the interpretations instead of providing a general implementation of data type handling operations.
Furthermore, our implementation does not require a generic function to trace all constructors, resulting in problems with (mutual) recursion.
\Citet{young_adding_2021} added pattern matching to a deeply \gls{EDSL} using a compiler plugin.
\Citet{duregard_embedded_2011} wrote a parser generator using \gls{TH} and the custom quasiquoting facilities.
From a specification of the grammar, given in verbatim using a custom quasiquoter, a parser is generated at compile time.
\Citet{shioda_libdsl_2014} used metaprogramming in the D programming language to create a \gls{DSL} toolkit.
-They also programmatically generate parsers and a backend for either compiling or interpreting the \gls{IR}.
+They also programmatically generate parsers and an interpretation for either compiling or interpreting the \gls{IR}.
\Citet{blanchette_liquid_2022} use \gls{TH} to simplify the development of Liquid \gls{HASKELL} proofs.
\Citet{folmer_high-level_2022} used \gls{TH} to synthesize C$\lambda$aSH \citep{baaij_digital_2015} \glspl{AST} to be processed.
In similar fashion, \citet{materzok_generating_2022} used \gls{TH} to translate YieldFSM programs to {C$\lambda$aSH}.
repositories / phd-thesis.git / blobdiff