Specifying model transformations by direct manipulation using concrete visual notations and interactive recommendations

Avazpour, Iman; Grundy, John; Grunske, Lars

doi:10.1016/j.jvlc.2015.02.005

Cited by 15 publications

(19 citation statements)

References 42 publications

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“…Regarding the completion of model transformations, CONVErT [10] synthesises transformation code starting from examples of source and target models and their correspondences. Correspondences are specified manually, but there is also a recommender of likely correspondences based on similarity heuristics such as the name, structure and neighbourhood of model elements.…”

Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

Recommender systems in model-driven engineering

et al. 2021

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Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

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Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

Recommender systems in model-driven engineering

et al. 2021

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“…An alternative approach to automated transformation synthesis (MTBE and MTBD) is proposed by Avazpour et al [2015]. The approach, CONVErT, uses visualization to allow a domain expert to interactively specify mappings between source and target elements in example model pairs.…”

Section: Related Workmentioning

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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“…In MTBE, transformations are defined as concrete syntax examples from which an underlying engine deduces rules expressed using a traditional MTL. In correspondence-based approaches [7,10,26,38,61], users explicitly state the correspondences between example source and target model elements. In demonstration-based approaches [29,50], transformation rules are exemplified by performing edit operations on the source model.…”

Section: St Ent Extmentioning

confidence: 99%

“…The single existing user study evaluating the usability of a MT approach addresses the CONVErT MTBE framework [7]. The presented evaluation is purely qualitative, and features a total of 15 participants which are asked to complete a transformation task using CONVErT and provide a subjective account of their experience via a questionnaire.…”

Section: Usability In Model Transformationmentioning

confidence: 99%