Automated Patch Generation for Fixing Semantic Errors in ATL Transformation Rules
Zahra VaraminyBahnemiry, Jessie Carbonnel, Khalid Belharbi, Houari A. Sahraoui · 2021
With the growing popularity of the MDE paradigm, model transformations are becoming more and more complex. ATL transformations, in particular, are error-prone due to the declarative nature of the language and the dependency towards the involved metamodels. To alleviate the burden of developers, we propose, in this paper, an approach for fixing semantic errors in ATL transformation rules without predefined patch templates for specific error types. In a first step, our approach determines the rules that are likely to contain errors starting from the discrepancy between the expected and produced outputs of test cases. Then, a second step allows to generate candidate patches for these errors using a multiobjective optimization algorithm, guided by the same test cases. In a preliminary evaluation, we show that our approach can fix most of the errors for transformations with one or two errors. For those with multiple errors, more iterations are necessary to fix some of the errors.