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Improving repair of semantic ATL errors using a social diversity metric

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Abstract

Model transformations play an essential role in the model-driven engineering paradigm. However, writing a correct transformation requires the user to understand both what the transformation should do and how to enact that change in the transformation. This easily leads to syntactic and semantic errors in transformations which are time-consuming to locate and fix. In this article, we extend our evolutionary algorithm (EA) approach to automatically repair transformations containing multiple semantic errors. To prevent the fitness plateaus and the single fitness peak limitations from our previous work, we include the notion of social diversity as an objective for our EA to promote repair patches tackling errors that are less covered by the other patches of the population. We evaluate our approach on four ATL transformations, which have been mutated to contain up to five semantic errors simultaneously. Our evaluation shows that integrating social diversity when searching for repair patches improves the quality of those patches and speeds up the convergence even when up to five semantic errors are involved.

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Notes

  1. https://www.eclipse.org/atl/atlTransformations/.

  2. https://www.eclipse.org/atl/atlTransformations/#Class2Relational.

  3. https://eclipse.dev/atl/documentation/.

  4. Note that there is a risk that a patch introduces new errors not seen in the original test suite. This situation is not covered in this objective, but instead by objective 2 (Sect. 3.2.2) which penalizes patches that create many errors.

  5. Available online at https://github.com/jgalasso/RepairingATLSemanticErrors.

  6. Experimental data is available at https://github.com/jgalasso/faulty-ATL-transformations.

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Acknowledgements

This work was partially performed while the second and third authors were employed at the Université de Montréal.

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Authors and Affiliations

  1. Département d’Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, 3150 Jean Brillant St, Montreal, Quebec, H3T 1N8, Canada

    Zahra VaraminyBahnemiry & Houari Sahraoui

  2. Department of Electrical and Computer Engineering (ECE), McGill University, 3480 University Street, Montreal, Quebec, H3A 0E9, Canada

    Jessie Galasso

  3. Département de Génie Informatique et Génie Logiciel (GIGL), Polytechnique Montréal, 2700 Tour Rd, Montreal, Quebec, H3T 1J4, Canada

    Bentley Oakes

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  1. Zahra VaraminyBahnemiry
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Correspondence to Houari Sahraoui.

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Communicated by Antonio Vallecillo.

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VaraminyBahnemiry, Z., Galasso, J., Oakes, B. et al. Improving repair of semantic ATL errors using a social diversity metric. Softw Syst Model (2024). https://doi.org/10.1007/s10270-024-01170-4

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