Abstract 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.

Abstract 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.

In model-driven software engineering, models are used in all phases of the development process. These models may get broken due to various editions during the modeling process. There are a number of existing tools that reduce the burden of manually dealing with correctness issues in models, however, most of these tools do not prioritize customization to follow user requirements nor allow the extension of their components to adapt to different model types. In this paper, we present an extensible model repair framework which enables users to deal with different types of models and to add their own repair preferences to customize the results. The framework uses customizable learning algorithms to automatically find the best sequence of actions for repairing a broken model according to the user preferences. As an example, we customize the framework by including as a preference a model distance metric, which allows the user to choose a more or less conservative repair. Then, we evaluate how this preference extension affects the results of the repair by comparing different distance metric calculations. Our experiment proves that extending the framework makes it more precise and produces models with better quality characteristics.