Interactive and Incremental Business Process Model Repair

Armas-Cervantes, Abel; Beest, Nick van; Rosa, Marcello La; Dumas, Marlon; García‐Bañuelos, Luciano

doi:10.1007/978-3-319-69462-7_5

Cited by 41 publications

(16 citation statements)

References 25 publications

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“…Inconsistencies appear at the stage of testing the test cases. If the observed behavior is recorded in an event log, it is possible to repair such a model [30]. However, our goal is to provide a prototype model based on the provided input data.…”

Section: Discussionmentioning

confidence: 99%

Automated Generation of Business Process Models using Constraint Logic Programming in Python

Paszun¹,

Wiśniewski²,

Kluza³

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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High complexity of business processes in real-life organizations is a constantly rising issue. In consequence, modeling a workflow is a challenge for process stakeholders. Yet, to facilitate this task, new methods can be implemented to automate the phase of process design. As a main contribution of this paper, we propose an approach to generate process models based on activities performed by the participants, where the exact order of execution does not need to be specified. Nevertheless, the goal of our method is to generate artificial workflow traces of a process using Constraint Programming and a set of predefined rules. As a final step, the approach was implemented as a dedicated tool and evaluated on a set of test examples that prove that our method is capable of creating correct process models.

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Section: Discussionmentioning

confidence: 99%

Automated Generation of Business Process Models using Constraint Logic Programming in Python

Paszun¹,

Wiśniewski²,

Kluza³

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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show abstract

“…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%

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

2022

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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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“…The approach achieves the maximum replay of the model at the minimum cost [34]. Unnecessary redundant behaviors in the process model are avoided as much as possible by manual repair compared with automatic operations [35]. They improve fitness reasonably while reducing the cost of repair as much as possible.…”

Section: B Model Repairmentioning

confidence: 99%

Real-Time Repair of Business Processes Based on Alternative Operations in Case of Uncertainty

et al. 2021

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Interactive and Incremental Business Process Model Repair

Cited by 41 publications

References 25 publications

Automated Generation of Business Process Models using Constraint Logic Programming in Python

Automated Generation of Business Process Models using Constraint Logic Programming in Python

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

Real-Time Repair of Business Processes Based on Alternative Operations in Case of Uncertainty

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