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model transformation by example Driven ATL transformation Rules Development Using model Differences 9th

9th International Joint Conference on Software Technologies (ICSOFT)

作者： Agirre, Joseba A. Sagardui, Goiuria Etxeberria, Leire Mondragon Unibertsitatea MGEP Arrasate Mondragon Spain

ISBN: (纸本)9783319255798;9783319255781

The use of model Driven Development (MDD) approach is increasing in industry. MDD approach raises the level of abstraction using models as main artifacts of software engineering processes. The development of model transformations is a critical step in MDD. Tasks for defining, specifying and maintaining model transformation rules can be complex in MDD. model transformation by example (MTBE) approaches have been proposed to ease the development process of transformation rules. Starting from pairs of example models the transformation rules are derived semi-automatically. The aim of our approach is to derive the adaptation operations that must be implemented in a legacy model transformation to fulfill a new transformation requirement. An MTBE approach and a tool to develop and evolve ATL transformation rules have been developed. Our approach derives the transformations operations automatically using execution traceability data and models differences. The developed MTBE approach can be applied to evolve legacy model transformations. The tool can be used with endogenous and exogenous model to model transformations. The approach has been validated with several model transformations and the results have been collected. A real case study is introduced to demonstrate the usefulness of the tool.

关键词： model driven development model transformation development model transformation by example model transformation execution trace model differences

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model transformation by example with Statistical Machine Translation 8th

Model Transformation by Example with Statistical Machine Tra...

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8th International Conference on model-Driven Engineering and Software Development (modelSWARD)

作者： Berramla, Karima Deba, El Abbassia Wu, Jiechen Sahraoui, Houari Benyamina, Abou El Hassen Oran 1 Univ Ahmed Ben Bella LAPECI Lab Oran Algeria Univ Montreal DIRO Montreal PQ Canada Ain Temouchent Univ Ctr Ain Temouchent Algeria

ISBN: (纸本)9789897584008

In the last decade, model-Driven Engineering (MDE) has experienced rapid growth in the software development community. In this context, model transformation occupies an important place that automates the transitions between development steps during the application production. To implement this transformation process, we require mastering languages and tools, but more importantly the semantic equivalence between the involved input and output metamodels. This knowledge is in general difficult to acquire, which makes transformation writing complex, time-consuming, and error-prone. In this paper, we propose a new model transformation by example approach to simplify model transformations, using Statistical Machine Translation (SMT). Our approach exploits the power of SMT by converting models in natural language texts and by processing them using models trained with IBM1 model.

关键词： model transformation model transformation by example Learning System SMT System IBM1 model

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MoTrans-BDI: Leveraging the Beliefs-Desires-Intentions agent architecture for collaborative model transformation by example

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JOURNAL OF COMPUTER LANGUAGES 2023年 74卷

作者： Siabdelhadi, Ahmed Chadli, Abdelhafid Cherroun, Hadda Ouared, Abdelkader Sahraoui, Houari Amar Telidji Univ Laghouat Lab Informat & Math LIM Laghouat Algeria Ibn Khaldoun Univ Tiaret Tiaret Algeria Univ Montreal Montreal PQ Canada

Due to the growing interest in model-driven system development techniques, the efficient design of automated model transformations between heterogeneous models has become a major challenge in software development. While a number of specialized languages have been proposed, aiming at specifying model transformations, there is currently no matured foundation for specifying transformations between such models that are based on designers/experts collaboration in order to propose a solution that satisfies users' requirements and application constraints. This transformation process is a complex task and must emulate how designers and experts with different perspectives behave and reflect about model transformations. In this paper, we propose a framework based on a novel approach for the specification and design of model transformations called MoTrans-BDI, which leverages Evolutionary Multi-Agent System (EMAS) to simulate designers' expertise for the transformation of models. Our approach is based on the Belief-Desire-Intention (BDI) agent model and the Contract Net Protocol where agents' beliefs feed from a series of transformation examples. The emphasis of using the specific model is the opportunity to produce a target model that may be composed of parts from different experts' designs. We first experimentally evaluate MoTrans-BDI on twelve handmade UML2REL model transformation problems. All types of agents are able to produce perfect target models compared to human experts' collaboratively-produced target models. Second, we empirically evaluate MoTrans-BDI's proposal in terms of response time on Star Schema Benchmark (SSB) queries. An application of MoTrans-BDI in the case of UML2REL transformation is performed to stress and highlight the transformation process steps.

关键词： model transformation by example model-driven engineering Belief-Desire-Intention model Multi-agent systems Contract Net Protocol

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A Preliminary Investigation into Automated model transformation Using Q-Learning: A Case Study 29

A Preliminary Investigation into Automated Model Transformat...

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29th International Computer Conference, Computer Society of Iran, CSICC 2025

作者： Nasrabadi, Delaram Nikbakht Samimi-Dehkordi, Leila Basiri, Mohammad ehsan Department of Computer Engineering Faculty of Engineering Shahrekord University Shahrekord Iran

ISBN: (纸本)9798331523114

model-Driven Engineering (MDE) emphasizes models as primary artifacts to enhance abstraction and automation in software development. However, manually defining model transformation rules is a time-consuming and error-prone task. This study proposes a Q-learning-based approach for automating model transformations by example, where transformation rules are dynamically learned through interaction with paired example models using reinforcement learning. The evaluation of the proposed method on UML-to-Java transformations demonstrates improvements in accuracy and scalability while reducing manual effort. The approach effectively addresses challenges such as model heterogeneities and dependency on high-quality examples, showcasing its potential to streamline and optimize model transformation processes. © 2025 IEEE.

关键词： model transformation model transformation by example model-driven engineering reinforcement learning

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Generating model transformation Rules from examples Using an Evolutionary Algorithm 12

Generating Model Transformation Rules from Examples Using an...

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27th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Faunes, Martin Sahraoui, Houari Boukadoum, Mounir Univ Montreal DIRO Montreal PQ Canada Univ Quebec Montreal Montreal PQ Canada

ISBN: (纸本)9781450312042

We propose an evolutionary approach to automatically generate model transformation rules from a set of examples. To this end, genetic programming is adapted to the problem of model transformation in the presence of complex input/output relationships (i.e., models conforming to meta-models) by generating declarative programs (i.e., transformation rules in this case). Our approach does not rely on prior transformation traces for the model-example pairs, and directly generates executable, many-to-many rules with complex conditions. The applicability of the approach is illustrated with the well-known problem of transforming UML class diagrams into relational schemas, using examples collected from the literature.

关键词： model transformation by example genetic programming

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Automating Code Generation for MDE using Machine Learning 23

Automating Code Generation for MDE using Machine Learning

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45th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Xue, Qiaomu Kings Coll London Strand London England

ISBN: (纸本)9798350322637

The overall aim of our research is to improve the techniques for synthesizing code generators in the model-Driven Engineering (MDE) context. Code generation is one of the main elements of model-Driven Engineering, involving transformation from specification models to produce executable code. A code generator is designed to reduce the manual program construction work used to implement a software system, but building a code generator itself still currently needs much manual effort. Meanwhile, existing code generators are typically not flexible to adjust for changing development requirements and are hard to reuse for different target languages. Therefore, we aim to provide techniques to improve the process of building code generators, and let them be more reusable. Currently, we researched the related new and traditional approaches for generating code and projects using AI for program translation, code completion or program generation. Based on this research we decided to focus on a symbolic machine learning method related to the programming-by-example concept to build code generators. We use this "Code Generation By example" (CGBE) concept with tree-to-tree structure mappings as the information format. CGBE has good performance in terms of training dataset size and time when applied to learning a UML-to-Java code generator, but further work is needed to extend it to generate different programming languages and to evaluate these cases, and to handle the optimisation of generated code.

关键词： Code Generation model transformation by example model-Driven Engineering Symbolic Machine Learning

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Evolving Legacy model transformations to Aggregate Non Functional Requirements of the Domain 3

Evolving Legacy Model Transformations to Aggregate Non Funct...

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3rd International Conference on model-Driven Engineering and Software Development (modelSWARD)

作者： Agirre, Joseba A. Sagardui, Goiuria Etxeberria, Leire Mondragon Univ Dept Comp Loramendi Mondragon Spain

ISBN: (纸本)9789897581366

The use of model Driven Development (MDD) is increasing in industry. When a Non Functional Requirement (NFR) not considered in the development must be added metamodels, models and also transformations are affected. Tasks for de ning and maintaining model transformation rules can be complex in MDD. model transformation by example (MTBE) approaches have been proposed to ease the development of transformation rules. In this paper an approach based on MTBE to derive the adaptation operations that must be implemented in a legacy model transformation when a NFR appears is presented. The approach derives semi-automatically the model transformations using execution traceability data and models differences. An example where access control property is integrated on a MDD system is introduced to demonstrate the usefulness of the tool to evolve model transformations.

关键词： model Driven Development model transformation model transformation by example model transformation by Demonstration Non Functional Requirements Design Patterns

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Multi-Step Learning and Adaptive Search for Learning Complex model transformations from examples

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2016年第3期25卷 20-20页

作者： Baki, Islem Sahraoui, Houari Univ Montreal Dept Informat & Rech Operat CP 6128Succursale Ctr Ville Montreal PQ H3C 3J7 Canada

model-driven engineering promotes models as main development artifacts. As several models may be manipulated during the software-development life cycle, model transformations ensure their consistency by automating model generation and update tasks. However, writing model transformations requires much knowledge and effort that detract from their benefits. To address this issue, model transformation by example (MTBE) aims to learn transformation programs from source and target model pairs supplied as examples. In this article, we tackle the fundamental issues that prevent the existing MTBE approaches from efficiently solving the problem of learning model transformations. We show that, when considering complex transformations, the search space is too large to be explored by naive search techniques. We propose an MTBE process to learn complex model transformations by considering three common requirements: element context and state dependencies and complex value derivation. Our process relies on two strategies to reduce the size of the search space and to better explore it, namely, multi-step learning and adaptive search. We experimentally evaluate our approach on seven model transformation problems. The learned transformation programs are able to produce perfect target models in three transformation cases, whereas precision and recall values larger than 90% are recorded for the four remaining cases.

关键词： Design Algorithms Performance Genetic programming model-driven engineering model transformation model transformation by example simulated annealing

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