Social Diversity for ATL Repair

TL;DR

This paper introduces a search-based method utilizing social diversity to automatically repair ATL transformation programs with multiple semantic errors, improving repair quality and convergence speed.

Contribution

It presents a novel approach that leverages social diversity to enhance the automatic repair of complex, error-prone transformation programs in Model-Driven Engineering.

Findings

Improves repair quality of ATL programs with multiple errors

Speeds up convergence in the repair process

Effective on programs with up to five semantic errors

Abstract

Model transformations play an essential role in the Model-Driven Engineering paradigm. Writing a correct transformation program requires to be proficient with the source and target modeling languages, to have a clear understanding of the mapping between the elements of the two, as well as to master the transformation language to properly describe the transformation. Transformation programs are thus complex and error-prone, and finding and fixing errors in such programs typically involve a tedious and time-consuming effort by developers. In this paper, we propose a novel search-based approach to automatically repair transformation programs containing many semantic errors. To prevent the fitness plateaus and the single fitness peak limitations, we leverage the notion of social diversity to promote repair patches tackling errors that are less covered by the other patches of the population. We evaluate our approach on 71 semantically incorrect transformation programs written in ATL, and containing up to five semantic errors simultaneously. The evaluation shows that integrating social diversity when searching for repair patches allows to improve the quality of those patches and to speed up the convergence even when up to five semantic errors are involved.