Formalised EMFTVM bytecode language for sound verification of model transformations

Cheng, Zheng; Monahan, Rosemary; Power, James F.

doi:10.1007/s10270-016-0553-x

Cited by 5 publications

(4 citation statements)

References 56 publications

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“…Set, OrderedSet, Sequence, Bag), and 78 OCL operations on data types, including the forAll, collect, select, and reject iterators on collections. Refining mode (that uses in-place scheduling) is supported by integrating our previous work [14]. The imperative and recursive aspects of ATL are currently not considered.…”

Section: Discussionmentioning

confidence: 99%

Slicing ATL model transformations for scalable deductive verification and fault localization

Cheng

Tisi

2018

Int J Softw Tools Technol Transfer

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Model-driven engineering (MDE) is increasingly accepted in industry as an effective approach for managing the full life cycle of software development. In MDE, software models are manipulated, evolved and translated by model transformations (MT), up to code generation. Automatic deductive verification techniques have been proposed to guarantee that transformations satisfy correctness requirements (encoded as transformation contracts). However, to be transferable to industry, these techniques need to be scalable and provide the user with easily accessible feedback. In MT-specific languages like ATL, we are able to infer static trace information (i.e. mappings among types of generated elements and rules that potentially generate these types). In this paper we show that this information can be used to decompose the MT contract and, for each subcontract , slice the MT to the only rules that may be responsible for fulfilling it. Based on this contribution, we design a fault localization approach for MT, and a technique to significantly enhance scalability when verifying large MTs against a large number of contracts. We implement both these algorithms as extensions of the VeriATL verification system, and we show by experimentation that they increase its industry-readiness.

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Section: Discussionmentioning

confidence: 99%

Slicing ATL model transformations for scalable deductive verification and fault localization

Cheng

Tisi

2018

Int J Softw Tools Technol Transfer

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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%

“…The goal is to produce an alternative implementation of ATL in Maude. Cheng et al develop an operational semantics for the ATL bytecode [14,15], which is used for translation validating axiomatic semantics of ATL transformations. Boronat also proposes an operational (big-step) semantics for ATL [9].…”

Section: Related Workmentioning

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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“…The selection criterion was to choose transformations written by a third-party (except UML2Intalio), with no typing errors (or very easily fixable ones) to avoid introducing a bias. The first five transformations are publicly available in the ATL Zoo, HSM2FSM is used as a case study in [Cheng et al 2018] to validate a verification method for model transformations, and UML2Intalio is used in [Sánchez Cuadrado et al 2018] to assess the validity and completeness of a catalogue of quick fixes for ATL. These transformations cover 71% of the ATL constructs, measured by extracting the footprint of the transformation definitions with respect to the underlying ATL meta-model.…”

Section: Evaluation Setupmentioning

confidence: 99%

Automated Reuse of Model Transformations through Typing Requirements Models

Lara

Guerra

Ruscio

et al. 2019

ACM Trans. Softw. Eng. Methodol.

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Model transformations are key elements of model-driven engineering, where they are used to automate the manipulation of models. However, they are typed with respect to concrete source and target meta-models, making their reuse for other (even similar) meta-models challenging. To improve this situation, we propose capturing the typing requirements for reusing a transformation with other meta-models by the notion of a typing requirements model (TRM). A TRM describes the prerequisites that a model transformation imposes on the source and target meta-models to obtain a correct typing. The key observation is that any meta-model pair that satisfies the TRM is a valid reuse context for the transformation at hand. A TRM is made of two domain requirement models (DRMs) describing the requirements for the source and target meta-models, and a compatibility model expressing dependencies between them. We define a notion of refinement between DRMs and see meta-models as a special case of DRM. We provide a catalogue of valid refinements and describe how to automatically extract a TRM from an ATL transformation. The approach is supported by our tool TOTEM. We report on two experiments—based on transformations developed by third parties and meta-model mutation techniques—validating the correctness and completeness of our TRM extraction procedure and confirming the power of TRMs to encode variability and support flexible reuse.

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Formalised EMFTVM bytecode language for sound verification of model transformations

Cited by 5 publications

References 56 publications

Slicing ATL model transformations for scalable deductive verification and fault localization

Slicing ATL model transformations for scalable deductive verification and fault localization

Deep specification and proof preservation for the CoqTL transformation language

Automated Reuse of Model Transformations through Typing Requirements Models

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