Personalized and Automatic Model Repairing using Reinforcement Learning

Barriga, Angela; Rutle, Adrian; Heldal, Rogardt

doi:10.1109/models-c.2019.00030

Cited by 24 publications

(24 citation statements)

References 7 publications

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“…The chances of breaking a model increase with collaborative modeling activities, depending on the number of changes in software requirements [BRH18], and the size of the conceptual domain to be engineered. This domain model might have become invalid at any stage of the modeling activity 1 .…”

Section: Running Examplementioning

confidence: 99%

“…In previous work, we introduced PARMOREL (Personalized and Automatic Repair of MOdels using REinforcement Learning) [BRH18,BRH19], an approach that provides personalized and automatic repair of software models using reinforcement learning (RL) [TL00]. PARMOREL finds a sequence of repairing actions according to preferences introduced by the user without considering objective measures such as quality characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Model Repair with Quality-Based Reinforcement Learning.

Iovino¹,

Rodríguez²,

Rutle³

et al. 2020

JOT

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Domain modeling is a core activity in Model-Driven Engineering, and these models must be correct. A large number of artifacts may be constructed on top of these domain models, such as instance models, transformations, and editors. Similar to any other software artifact, domain models are subject to the introduction of errors during the modeling process. There are a number of existing tools that reduce the burden of manually dealing with correctness issues in models. Although various approaches have been proposed to support the quality assessment of modeling artifacts in the past decade, the quality of the automatically repaired models has not been the focus of repairing processes. In this paper, we propose the integration of an automatic evaluation of domain models based on a quality model with a framework for personalized and automatic model repair. The framework uses reinforcement learning to find the best sequence of actions for repairing a broken model.

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Section: Running Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Repair with Quality-Based Reinforcement Learning.

Iovino¹,

Rodríguez²,

Rutle³

et al. 2020

JOT

Self Cite

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“…In this way, Pl-nodes not satisfying the condition c, are deleted. We obtain a repair program for d 1 is a repair program for e 1 ∧ e 2 = n i=1 d i . An illustration of this part of the proof is given in Figure 4.…”

Section: Definition 8 (Preservationmentioning

confidence: 99%

“…In Barriga et al 2019 [1], an algorithm for model repair based on EMF is presented, which relies on reinforcement learning. For each error in the model, a so-called Q-table is constructed, storing a weight for each error, and repair action.…”

Section: Related Workmentioning

confidence: 99%

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Graph Repair and its Application to Meta-Modeling

Sandmann

2020

Electron. Proc. Theor. Comput. Sci.

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Model repair is an essential topic in model-driven engineering. We present typed graph-repair programs for specific conditions; application to any typed graph yields a typed graph satisfying the condition. A model graph based on the Eclipse Modeling Framework (EMF), short EMF-model graph, is a typed graph satisfying some structural EMF-constraints. Application of the results to the EMF-world yields model-repair programs for EMFk constraints, a first-order variant of EMF constraints; application to any typed graph yields an EMFk model graph. From these results, we derive results for EMF model repair. * This work is partly supported by the German Research Foundation (DFG), Grants HA 2936/4-2 and TA 2941/3-2 (Meta-Modeling and Graph Grammars: Generating Development Environments for Modeling Languages).

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