Surveying the Corpus of Model Resolution Strategies for Metamodel Evolution

Hebig, Regina; Khelladi, Djamel Eddine; Bendraou, Reda

doi:10.1109/apsec.2015.40

Cited by 7 publications

(5 citation statements)

References 21 publications

(39 reference statements)

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“…Nonetheless, there seem to be common sources of error, when assessing whether a change type is breaking or non-breaking. For example, in [76] we found that misclassifications seem to stem from situations where special cases of change types are not considered. In consequence, we found change types that are treated by some approaches as non-breaking while other approaches treated them as breaking and provided resolution strategies [76] .…”

Section: Supported Benefit Classesmentioning

confidence: 94%

“…Please note that the degree of automation is in literature often addressed with a classification of change types into "non-breaking", "breaking and resolvable" and "breaking and non-resolvable" change types. However, during this survey and a related one [76] we found some serious flaws in that classification and its application that made us reconsider its use.…”

Section: Supported Benefit Classesmentioning

confidence: 95%

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Approaches to Co-Evolution of Metamodels and Models: A Survey

Hebig¹,

Khelladi²,

Bendraou³

2017

IIEEE Trans. Software Eng.

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Modeling languages, just as all software artifacts, evolve. This poses the risk that legacy models of a company get lost, when they become incompatible with the new language version. To address this risk, a multitude of approaches for metamodel-model co-evolution were proposed in the last 10 years. However, the high number of solutions makes it difficult for practitioners to choose an appropriate approach. In this paper, we present a survey on 31 approaches to support metamodel-model co-evolution. We introduce a taxonomy of solution techniques and classify the existing approaches. To support researchers, we discuss the state of the art, in order to better identify open issues. Furthermore, we use the results to provide a decision support for practitioners, who aim to adopt solutions from research.

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Section: Supported Benefit Classesmentioning

confidence: 94%

Section: Supported Benefit Classesmentioning

confidence: 95%

Approaches to Co-Evolution of Metamodels and Models: A Survey

Hebig¹,

Khelladi²,

Bendraou³

2017

IIEEE Trans. Software Eng.

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“…A PartialChange concerns a model element (concernedElt). We list in the ChangePartialKind enumeration the following partial changes inspired from the work in the literature [32]: • A global change has a high priority; so, it is inserted at the top of the stChanges stack since it changes the whole structure of a viewpoint, and will be processed first. • A partial change:…”

Section: Activity 1: Detect Changesmentioning

confidence: 99%

A Group Decision-Making Approach for Global Consistency of Heterogeneous Models

Bennani

Hamlaoui

Coulette

et al. 2022

Int. J. Info. Tech. Dec. Mak.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…However, coevolution approaches repair a particular type of inconsistencies w.r.t. metamodels conformance [19,20]. Here we focus on model repair.…”

Section: Related Workmentioning

confidence: 99%

Detecting and exploring side effects when repairing model inconsistencies

Khelladi

Kretschmer²,

Egyed³

2019

Proceedings of the 12th ACM SIGPLAN International Conference on Software Language Engineering

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When software models change, developers often fail in keeping them consistent. Automated support in repairing inconsistencies is widely addressed. Yet, merely enumerating repairs for developers is not enough. A repair can as a side effect cause new unexpected inconsistencies (negative) or even fix other inconsistencies as well (positive). To make matters worse, repairing negative side effects can in turn cause further side effects. Current approaches do not detect and track such side effects in depth, which can increase developers' effort and time spent in repairing inconsistencies. This paper presents an automated approach for detecting and tracking the consequences of repairs, i.e. side effects. It recursively explores in depth positive and negative side effects and identifies paths and cycles of repairs. This paper further ranks repairs based on side effect knowledge so that developers may quickly find the relevant ones. Our approach and its tool implementation have been empirically assessed on 14 case studies from industry, academia, and GitHub. Results show that both positive and negative side effects occur frequently. A comparison with three versioned models showed the usefulness of our ranking strategy based on side effects. It showed that our approach's top prioritized repairs are those that developers would indeed choose. A controlled experiment with 24 participants further highlights the significant influence of side effects and of our ranking of repairs on developers. Developers who received side effect knowledge chose far more repairs with positive side effects and far less with negative side effects, while being 12.3% faster, in contrast to developers who did not receive side effect knowledge. CCS Concepts • Software and its engineering → Model-driven software engineering; Software maintenance tools.

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Surveying the Corpus of Model Resolution Strategies for Metamodel Evolution

Cited by 7 publications

References 21 publications

Approaches to Co-Evolution of Metamodels and Models: A Survey

Approaches to Co-Evolution of Metamodels and Models: A Survey

A Group Decision-Making Approach for Global Consistency of Heterogeneous Models

Detecting and exploring side effects when repairing model inconsistencies

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