CoqTL: a Coq DSL for rule-based model transformation

Cheng, Zheng; Tisi, Massimo; Douence, Rémi

doi:10.1007/s10270-019-00765-6

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…In the evaluation section (Section 5) we will use a more realistic example, i.e. the transformation of hierarchical state machines into flat state machines, where CoqTL has been used to prove interesting properties in previous work [19].…”

Section: Running Examplementioning

confidence: 99%

Deep specification and proof preservation for the CoqTL transformation language

Cheng

Tisi

2022

Softw Syst Model

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Executable engines for relational model-transformation languages evolve continuously because of language extension, performance improvement and bug fixes. While new versions generally change the engine semantics, end-users expect to get backward-compatibility guarantees, so that existing transformations do not need to be adapted at every engine update.The CoqTL model-transformation language allows users to define model transformations, theorems on their behavior and machine-checked proofs of these theorems in Coq. Backward-compatibility for CoqTL involves also the preservation of these proofs. However, proof preservation is challenging, as proofs are easily broken even by small refactorings of the code they verify.In this paper we present the solution we designed for the evolution of CoqTL. We provide a deep specification of the transformation engine, including a set of theorems that must hold against the engine implementation. Then, at each milestone in the engine development, we certify the new version of the engine against this specification, by providing proofs of the impacted theorems. The certification formally guarantees end-users that all the proofs they write using the provided theorems will be preserved through engine updates.We illustrate the structure of the deep specification theorems, we produce a machine-checked certification of three versions of CoqTL against it, and we show examples of user proofs that leverage this specification and are thus preserved through the updates. Finally, we discuss the evolution of the deep specification by an extension mechanism, we present an evolution that introduces trace links in the specification, and we show which user proofs are preserved through specification evolutions.

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Section: Running Examplementioning

confidence: 99%

Deep specification and proof preservation for the CoqTL transformation language

Cheng

Tisi

2022

Softw Syst Model

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“…Steinberg et al 2008), 5 times Acceleo (Eclipse Foundation, Inc. 2019) and 4 times Xtend (Bettini 2016). The languages C++, C# , CoqTL (Cheng et al 2020) Figure 6 shows the distribution of the hours per month in which a transformation language is used (cf. Q5) split into two plots.…”

Section: General Informationmentioning

confidence: 99%

A Survey on the Relevance of the Performance of Model Transformations.

Groner¹,

Juhnke²,

Götz³

et al. 2021

JOT

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In Model-Driven Engineering (MDE) model transformation languages are used to describe important operations on models.Such domain-specific languages are specially developed to describe transformation rules, according to which an output model should be generated from an input model. In comparison to these domain-specific languages, techniques to analyze and improve the performance of programs written in a general-purpose language, such as Java or C, are well known. However, are such techniques also needed for model transformation languages? Problem. Since these languages are only used in certain domains; the first question is whether performance is at all relevant for model transformations and whether techniques similar to those used to analyze and improve the performance of general-purpose languages are needed. Research in the performance of model transformations focuses mainly on comparing the performance of different languages or different definition styles or optimizing the engine that executes the transformation. However, it is not clear to what extent these efforts can mitigate or prevent performance issues, and there is also a lack of studies that examine to what extend the performance of transformations is relevant. Method. In order to close this gap and to answer the initial question about the relevance of performance, we conducted an online survey. For this purpose, we developed a questionnaire and identified 649 authors as potential participants based on a Systematic Literature Review (SLR) on a selection of model transformation languages. Additionally, we were able to acquire four further potential participants by advertising our study. In total, 84 participants took part in our survey. We used statistical tests such as Kendall's τ c , the Kruskal-Wallis-Test and the Mann-Whitney-U-Test to evaluate our hypotheses on relevant factors for the performance of model transformations. Results. The results show that specific performance is desired and that there is a willingness to improve performance. In this regard, we identified a need for insights necessary to better understand how a transformation is performed in order to be able to improve its performance. Furthermore, we investigated with the help of hypotheses tests the possible influencing factors that cause participants to try to analyze or improve the performance of model transformations. The main results of the hypotheses tests are that the satisfaction with the execution time, the size of the models used, the relevance of whether a specific execution time is not exceeded in the average case, and the knowledge of how a transformation engine executes a transformation are relevant factors.

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“…The element section includes standard Gallina code to instantiate the new element specifying the value of its attributes (line 11). The links section contains standard Gallina code to instantiate links outgoing from the new element (lines [14][15][16][17].…”

Section: Running Examplementioning

confidence: 99%

“…Models and Metamodels. The deep specification that we introduce for CoqTL reuses the definition of models and metamodels in Coq from [15]. There, a model is defined by a Coq typeclass as a list of ModelElements and a list of ModelLinks.…”

Section: Deep Specification For Coqtlmentioning

confidence: 99%

“…instantiateRuleOnPattern generates target elements by transforming the source pattern using only the given rule. Since CoqTL supports iterative rules (executed once for every value of a given iterator [15]), instantiateRuleOnPattern is obtained by concatenating the results of instantiateIterationOnPattern for every iteration. In the same way instantiateIterationOnPattern is obtained by concatenating the result of the creation of each output element in the rule, by instantiateElementOnPattern.…”

Section: Deep Specification For Coqtlmentioning

confidence: 99%

See 1 more Smart Citation

Certifying a rule-based model transformation engine for proof preservation

Cheng

Tisi

Hotonnier

2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems

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Executable engines for relational model-transformation languages evolve continuously because of language extension, performance improvement and bug fixes. While new versions generally change the engine semantics, end-users expect to get backward-compatibility guarantees, so that existing transformations do not need to be adapted at every engine update. The CoqTL model-transformation language allows users to define model transformations, theorems on their behavior and machinechecked proofs of these theorems in Coq. Backward-compatibility for CoqTL involves also the preservation of these proofs. However, proof preservation is challenging, as proofs are easily broken even by small refactorings of the code they verify. In this paper we present the solution we designed for the evolution of CoqTL, and by extension, of rule-based transformation engines. We provide a deep specification of the transformation engine, including a set of theorems that must hold against the engine implementation. Then, at each milestone in the engine development, we certify the new version of the engine against this specification, by providing proofs of the impacted theorems. The certification formally guarantees end-users that all the proofs they write using the provided theorems will be preserved through engine updates. We illustrate the structure of the deep specification theorems, we produce a machine-checked certification of three versions of CoqTL against it, and we show examples of user theorems that leverage this specification and are thus preserved through the updates. CCS CONCEPTS • Theory of computation → Logic; • Software and its engineering → Software notations and tools.

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CoqTL: a Coq DSL for rule-based model transformation

Cited by 7 publications

References 36 publications

Deep specification and proof preservation for the CoqTL transformation language

Deep specification and proof preservation for the CoqTL transformation language

A Survey on the Relevance of the Performance of Model Transformations.

Certifying a rule-based model transformation engine for proof preservation

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