An extensible framework for customizable model repair

Barriga, Angela; Heldal, Rogardt; Iovino, Ludovico; Marthinsen, Magnus; Rutle, Adrian

doi:10.1145/3365438.3410957

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…The existing model repair approaches tackle this problem from different perspectives. For example, some approaches try to find the repaired version of the model closer to the original one in terms of structure [4], to get the repair actions from the ones previously applied in the model [23], or by directly asking the user which repair actions they prefer, either by interacting with the user during the repair process [6,24] or by asking for her preferences before the repair starts [25]. The goal of AI-powered model repair is to take advantage of the potential of AI to find the most optimal repair actions to manage the issues in a model.…”

Section: Model Repairmentioning

confidence: 99%

“…Over the last years, the authors of this paper have developed PARMOREL [25,[42][43][44][45], a customizable and extensible model repair framework that enables users to deal with different issues in different types of models.…”

Section: Markov Decision Processmentioning

confidence: 99%

“…The framework consists of a series of modules that can be implemented by users. Modules allow personalizing the type of models to repair, the issues to be addressed, the repair actions, the RL algorithm to use, and the repair preferences [25]. Instead of having a specific tool for each kind of issue and each type of model, PARMOREL works as a unified extensible framework, which can be extended to support the new issues that modelers need to solve in their models.…”

Section: Markov Decision Processmentioning

confidence: 99%

“…Mussbacher et al [72] discuss that most AI applications in MDE are tailor-made for solving specific problems. However, regarding model repair, the diversity of type of models, issues to solve, and how to solve them make time-consuming to build individual solutions to address each of these nuances [25]. This diversity is a challenge for scalability and producing tailor-made solutions slows the development of the AI model repair field.…”

Section: Generalitymentioning

confidence: 99%

“…As stated in Sect. 3, the authors of this paper have developed over the last years PARMOREL, a framework for automatic and personalizable model repair based on RL [25]. We consider that presenting our experience might be useful for other researchers, hence, in this section, we discuss the lessons we learned while developing PARMOREL, connecting them to the challenges presented in Sect.…”

Section: Lessons Learned While Developing Parmorelmentioning

confidence: 99%

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AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

2022

Self Cite

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Artificial intelligence has already proven to be a powerful tool to automate and improve how we deal with software development processes. The application of artificial intelligence to model-driven engineering projects is becoming more and more popular; however, within the model repair field, the use of this technique remains mostly an open challenge. In this paper, we explore some existing approaches in the field of AI-powered model repair. From the existing approaches in this field, we identify a series of challenges which the community needs to overcome. In addition, we present a number of research opportunities by taking inspiration from other fields which have successfully used artificial intelligence, such as code repair. Moreover, we discuss the connection between the existing approaches and the opportunities with the identified challenges. Finally, we present the outcomes of our experience of applying artificial intelligence to model repair.

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Section: Model Repairmentioning

confidence: 99%

Section: Markov Decision Processmentioning

confidence: 99%

Section: Markov Decision Processmentioning

confidence: 99%

Section: Generalitymentioning

confidence: 99%

Section: Lessons Learned While Developing Parmorelmentioning

confidence: 99%

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AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

2022

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PARMOREL: a framework for customizable model repair

et al. 2022

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In model-driven software engineering, models are used in all phases of the development process. These models must hold a high quality since the implementation of the systems they represent relies on them. Several existing tools reduce the burden of manually dealing with issues that affect models’ quality, such as syntax errors, model smells, and inadequate structures. However, these tools are often inflexible for customization and hard to extend. This paper presents a customizable and extensible model repair framework, PARMOREL, that enables users to deal with different issues in different types of models. The framework uses reinforcement learning to automatically find the best sequence of actions for repairing a broken model according to user preferences. As proof of concept, we repair syntactic errors in class diagrams taking into account a model distance metric and quality characteristics. In addition, we restore inter-model consistency between UML class and sequence diagrams while improving the coupling qualities of the sequence diagrams. Furthermore, we evaluate the approach on a large publicly available dataset and a set of real-world inspired models to show that PARMOREL can decide and pick the best solution to solve the issues present in the models to satisfy user preferences.

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On the use of domain knowledge for process model repair

Revoredo

2022

Softw Syst Model

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Process models are important for supporting organizations in documenting, understanding and monitoring their business. When these process models become outdated, they need to be revised to accurately describe the new status quo of the processes in the organization. Process model repair techniques help at automatically revising the existing model from behavior traced in event logs. So far, such techniques have focused on identifying which parts of the model to change and how to change them, but they do not use knowledge from practitioners to inform the revision. As a consequence, fragments of the model may change in a way that defies existing regulations or represents outdated information that was wrongly considered from the event log. This paper uses concepts from theory revision to provide formal foundations for process model repair that exploits domain knowledge. Specifically, it conceptualizes (1) what are unchangeable fragments in the model and (2) the role that various traces in the event log should play when it comes to model repair. A scenario of use is presented that demonstrates the benefits of this conceptualization. The current state of existing process model repair techniques is compared against the proposed concepts. The results show that only two existing techniques partially consider the concepts presented in this paper for model repair.

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An extensible framework for customizable model repair

Cited by 7 publications

References 26 publications

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

PARMOREL: a framework for customizable model repair

On the use of domain knowledge for process model repair

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