CoqTL: an Internal DSL for Model Transformation in Coq - Archive ouverte HAL Access content directly
Conference Papers Year :

CoqTL: an Internal DSL for Model Transformation in Coq

(1, 2) , (3, 1, 2)
1
2
3

Abstract

In model-driven engineering, model transformation (MT) verification is essential for reliably producing software artifacts. While recent advancements have enabled automatic Hoare-style verification for non-trivial MTs, there are certain verification tasks (e.g. induction) that are intrinsically difficult to automate. Existing tools that aim at simplifying the interactive verification of MTs typically translate the MT specification (e.g. in ATL) and properties to prove (e.g. in OCL) into an interactive theorem prover. However, since the MT specification and proof phases happen in separate languages, the proof developer needs a deep knowledge of the translation logic. Naturally any error in the MT translation could cause unsound verification, i.e. the MT executed in the original environment may have different semantics from the verified MT. We propose an alternative solution by designing and implementing an internal domain specific language, namely CoqTL, for the specification of declarative MTs directly in the Coq interactive theorem prover. Expressions in CoqTL are written in Gallina (the specification language of Coq), increasing the possibilities of reuse of native Coq libraries in the transformation definition and proof. In this paper we introduce CoqTL, we evaluate its practical applicability on a case study, and identify its limitations.
Fichier principal
Vignette du fichier
main.pdf (397.87 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01828344 , version 1 (03-07-2018)

Identifiers

Cite

Massimo Tisi, Zheng Cheng. CoqTL: an Internal DSL for Model Transformation in Coq. ICMT 2018 - 11th International Conference on Theory and Practice of Model Transformations, Jun 2018, Toulouse, France. pp.142-156, ⟨10.1007/978-3-319-93317-7_7⟩. ⟨hal-01828344⟩
303 View
421 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More