Improving Model Repair through Experience Sharing

By: Angela Barriga, Adrian Rutle, Rogardt Heldal


In model-driven software engineering, models are used in all phases of the development process. These models may get broken due to various editions throughout their life-cycle. There are already approaches that provide an automatic repair of models, however, the same issues might not have the same solutions in all contexts due to different user preferences and business policies. Personalization would enhance the usability of automatic repairs in different contexts, and by reusing the experience from previous repairs we would avoid duplicated calculations when facing similar issues. By using reinforcement learning we have achieved the repair of broken models allowing both automation and personalization of results. In this paper, we propose transfer learning to reuse the experience learned from each model repair. We have validated our approach by repairing models using different sets of personalization preferences and studying how the repair time improved when reusing the experience from each repair.


Model Repair, Reinforcement Learning, Transfer Learning

Cite as:

Angela Barriga, Adrian Rutle, Rogardt Heldal, “Improving Model Repair through Experience Sharing”, Journal of Object Technology, Volume 19, no. 2 (July 2020), pp. 13:1-21, doi:10.5381/jot.2020.19.2.a13.

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The JOT Journal   |   ISSN 1660-1769   |   DOI 10.5381/jot   |   AITO   |   Open Access   |    Contact