Generating Large EMF Models Efficiently

Nassar, Nebras; Kosiol, Jens; Kehrer, Timo; Taentzer, Gabriele

doi:10.1007/978-3-030-45234-6_11

Cited by 10 publications

(4 citation statements)

References 27 publications

(50 reference statements)

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“…They generate multiple variants of the target system differing by injecting one bug in each. Furthermore, there are lines of research on generating benchmark models in the model-driven engineering domain [25,37,39,43] and performance testing of concrete software solutions [6,41,45]. Our approach fits this strategy, too.…”

Section: Background and Related Workmentioning

confidence: 98%

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A Generator Framework For Evolving Variant-Rich Software

Derks¹,

Strüber²,

Berger³

2021

Preprint

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Evolving software is challenging, even more when it exists in many different variants. Such software evolves not only in time, but also in space-another dimension of complexity. While evolution in space is supported by a variety of product-line and variability management tools, many of which originating from research, their level of evaluation varies significantly, which threatens their relevance for practitioners and future research. Many tools have only been evaluated on ad hoc datasets, minimal examples or available preprocessor-based product lines, missing the early clone & own phases and the re-engineering into configurable platforms-large parts of the actual evolution lifecycle of variant-rich systems. Our long-term goal is to provide benchmarks to increase the maturity of evaluating such tools. However, providing manually curated benchmarks that cover the whole evolution lifecycle and that are detailed enough to serve as ground truths, is challenging.We present the framework vpbench to generates source-code histories of variant-rich systems. Vpbench comprises several modular generators relying on evolution operators that systematically and automatically evolve real codebases and document the evolution in detail. We provide simple and more advanced generators-e.g., relying on code transplantation techniques to obtain whole features from external, real-world projects. We define requirements and demonstrate how vpbench addresses them for the generated version histories, focusing on support for evolution in time and space, the generation of detailed meta-data about the evolution, also considering compileability and extensibility. CCS CONCEPTS• Software and its engineering → Software product lines.

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Section: Background and Related Workmentioning

confidence: 98%

“…Unfortunately, no technique creates version histories. While a few papers proposed iterative techniques [11,25,30,35,39,42], all of them exclusively focus on the end result, intermediate steps are not part of the produced system. This contrasts version histories, where intermediate versions are necessary parts of the result.…”

Section: Background and Related Workmentioning

confidence: 99%

A Generator Framework For Evolving Variant-Rich Software

Derks¹,

Strüber²,

Berger³

2021

Preprint

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“…Some works have specifically focus on the efficiently generation of models. For instance, Nassar et al [68] automate the generation of valid EMF models. The metamodel is first translated to a rule-based model transformation system and then these rules generate the models.…”

Section: Related Workmentioning

confidence: 99%

Ant-colony optimization for automating test model generation in model transformation testing

Karimi,

Kolahdouz-Rahimi,

Troya

2024

Journal of Systems and Software

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“…GSCALER [90] and ReCoN [77] focus purely on graph scalability: given an input graph, these tools generate a similar graph with a certain number of nodes and edges. Similarly, [53] proposes a rule-based approach for scalable model generation which evaluates diversity and ensures adherence to metamodel constraints, but it does not handle extra additional WF constraints and excludes analysis of realistic nature. S3G2 [56] uses a MapReduce-based iterative algorithm to generate realistic and scalable multidimensional graphs, but it is demonstrated for a single domain (social networks) without consistency constraints.…”

Section: Learning-based Graph Generationmentioning

confidence: 99%

Automated generation of consistent, diverse and structurally realistic graph models

et al. 2021

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In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.

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Generating Large EMF Models Efficiently

Cited by 10 publications

References 27 publications

A Generator Framework For Evolving Variant-Rich Software

A Generator Framework For Evolving Variant-Rich Software

Ant-colony optimization for automating test model generation in model transformation testing

Automated generation of consistent, diverse and structurally realistic graph models

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