EDBT 2026 Demo / reviewers in the wild / expert
Utkarsh Dhandhania
dblp:334/4706
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-3187-3563ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Programming languages and type systems · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Programming languages and type systems
domain-specific languages |
0.6 | 1 | 2022 | Compositional embeddings of domain-specific languages · Proc. ACM Program. Lang. 2022 |
Programming languages and type systems › domain-specific languages
embedded domain-specific languages |
0.6 | 1 | 2022 | Compositional embeddings of domain-specific languages · Proc. ACM Program. Lang. 2022 |
Programming languages and type systems
language design |
0.6 | 1 | 2022 | Compositional embeddings of domain-specific languages · Proc. ACM Program. Lang. 2022 |
Methods — techniques the papers use, named apart from their topics
shallow embedding · 0.6pattern matching · 0.6deep embedding · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Compositional embeddings of domain-specific languagesabstractA common approach to defining domain-specific languages (DSLs) is via a direct embedding into a host language. There are several well-known techniques to do such embeddings, including shallow and deep embeddings. However, such embeddings come with various trade-offs in existing programming languages. Owing to such trade-offs, many embedded DSLs end up using a mix of approaches in practice, requiring a substantial amount of code, as well as some advanced coding techniques. In this paper, we show that the recently proposed Compositional Programming paradigm and the CP language provide improved support for embedded DSLs. In CP we obtain a new form of embedding, which we call a compositional embedding, that has most of the advantages of both shallow and deep embeddings. On the one hand, compositional embeddings enable various forms of linguistic reuse that are characteristic of shallow embeddings, including the ability to reuse host-language optimizations in the DSL and add new DSL constructs easily. On the other hand, similarly to deep embeddings, compositional embeddings support definitions by pattern matching or dynamic dispatching (including dependent interpretations, transformations, and optimizations) over the abstract syntax of the DSL and have the ability to add new interpretations. We illustrate an instance of compositional embeddings with a DSL for document authoring called ExT. The DSL is highly flexible and extensible, allowing users to create various non-trivial extensions easily. For instance, ExT supports various extensions that enable the production of wiki-like documents, LaTeX documents, vector graphics or charts. The viability of compositional embeddings for ExT is evaluated with three applications. Yaozhu Sun, Utkarsh Dhandhania, Bruno C. d. S. Oliveira |
Proc. ACM Program. Lang. | 2 |