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

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.

The End of Management Consulting as We Know it?

“…A company without an AI strategy, while its competitors are quickly advancing in the market, is like a Formula One (F1) race where all the competitors are driving high-tech F1 cars, but one team is driving a fast but regular streetcar. Just as the streetcar cannot compete with the advanced technology of the F1 cars, the company without an AI strategy will fall behind its competitors who are leveraging AI (Barriga, Rutle & Heldal, 2022;Belhadi, et al, 2021;Kersting, 2018). Are CEOs at risk to be replaced if they do not consider AI-powered strategy?…”

Validation and verification in domain-specific modeling method engineering: an integrated life-cycle view

“…The task classified as "repair" is most directly linked to V&V in that it suggests fixes for modeling errors and improves quality thereof. AI-driven approaches to model repair include for example [12] and [44]. More specifically, [44] proposed to leverage a combination of model finder techniques (such as Alloy) and machine learning techniques, whereby model finder techniques are used to generate a set of positive and negative examples with respect to certain quality properties, which can then subsequently be used in a learning process to arrive at a trained learner.…”