Learning Transformation Rules from Transformation Examples: An Approach Based on Relational Concept Analysis

Dolques, Xavier; Huchard, Marianne; Nebut, Clémentine; Reitz, Philippe

doi:10.1109/edocw.2010.32

Cited by 19 publications

(8 citation statements)

References 10 publications

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“…The primary intent is to reach model repairing with human quality without requiring supervision potentially. Various studies [20,25] use ML techniques to automatically infer model transformation rules from sets of source and target models. Metalearning is a technique that aims at using ML itself to automatically learn the most appropriate algorithms and parameters for an ML problem.…”

Section: Machine Learning In Mdementioning

confidence: 99%

Evaluation of a machine learning classifier for metamodels

et al. 2021

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Modeling is a ubiquitous activity in the process of software development. In recent years, such an activity has reached a high degree of intricacy, guided by the heterogeneity of the components, data sources, and tasks. The democratized use of models has led to the necessity for suitable machinery for mining modeling repositories. Among others, the classification of metamodels into independent categories facilitates personalized searches by boosting the visibility of metamodels. Nevertheless, the manual classification of metamodels is not only a tedious but also an error-prone task. According to our observation, misclassification is the norm which leads to a reduction in reachability as well as reusability of metamodels. Handling such complexity requires suitable tooling to leverage raw data into practical knowledge that can help modelers with their daily tasks. In our previous work, we proposed AURORA as a machine learning classifier for metamodel repositories. In this paper, we present a thorough evaluation of the system by taking into consideration different settings as well as evaluation metrics. More importantly, we improve the original AURORA tool by changing its internal design. Experimental results demonstrate that the proposed amendment is beneficial to the classification of metamodels. We also compared our approach with two baseline algorithms, namely gradient boosted decision tree and support vector machines. Eventually, we see that AURORA outperforms the baselines with respect to various quality metrics.

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Section: Machine Learning In Mdementioning

confidence: 99%

Evaluation of a machine learning classifier for metamodels

et al. 2021

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“…The approach is taken a step further by producing transformation rules while overcoming the need for transformation mappings ]. Another contribution, not initially intended for Dolques et al [2010]. The approach is based on relational concept analysis to learn transformation patterns.…”

Section: Related Workmentioning

confidence: 99%

“…MTBE approaches aim to derive transformation rules starting from a set of interrelated source and target model pairs. Over the past few years, different approaches have been proposed to achieve MT automation using examples with ad-hoc heuristics [Varró 2006;], inductive logic programming [Balogh and Varró 2009], particle swarm optimization [Kessentini et al 2008], relational concept analysis [Dolques et al 2010], genetic programming [Faunes et al 2012], and so on.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples

Baki

Sahraoui

2016

ACM Trans. Softw. Eng. Methodol.

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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. ACM Reference Format: Islem Baki and Houari Sahraoui. 2016. Multi-step learning and adaptive search for learning complex model transformations from examples.

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“…Finally, RCA was applied in Software Engineering in order to solve different problems: normalize (by factorization) a UML class model [19,16] or a UML use case model [10]; for refactoring [26]; learn model transformation patterns [9]; build directories of Web services or components [5]; structure [7] or analyze [2,18] the variability in software product lines.…”

Section: Background and Motivationmentioning

confidence: 99%

Relational Concept Analysis in Practice: Capitalizing on Data Modeling Using Design Patterns

Braud

Dolques

Huchard

et al. 2023

Lecture Notes in Computer Science

Self Cite

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Many applications of Formal Concept Analysis (FCA) and its diverse extensions have been carried out in recent years. Among these extensions, Relational Concept Analysis (RCA) is one approach for addressing knowledge discovery in multi-relational datasets. Applying RCA requires stating a question of interest and encoding the dataset into the input RCA data model, i.e. an Entity-Relationship model with only Boolean attributes in the entity description and unidirectional binary relationships. From the various concrete RCA applications, recurring encoding patterns can be observed, that we aim to capitalize taking software engineering design patterns as a source of inspiration. This capitalization work intends to rationalize and facilitate encoding in future RCA applications. In this paper, we describe an approach for defining such design patterns, and we present two design patterns: "Separate/Gather Views" and "Level Relations".

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Learning Transformation Rules from Transformation Examples: An Approach Based on Relational Concept Analysis

Cited by 19 publications

References 10 publications

Evaluation of a machine learning classifier for metamodels

Evaluation of a machine learning classifier for metamodels

Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples

Relational Concept Analysis in Practice: Capitalizing on Data Modeling Using Design Patterns

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