survey

Model Transformation Testing and Debugging: A Survey

Authors:
Javier Troya

ITIS Software, Universidad de Málaga, Malaga, Spain

ITIS Software, Universidad de Málaga, Malaga, Spain
View Profile

,
Sergio Segura

SCORE Lab, I3US Institute, Universidad de Sevilla, Seville, Spain

SCORE Lab, I3US Institute, Universidad de Sevilla, Seville, Spain
View Profile

,
Lola Burgueño

IN3, Open University of Catalonia, Barcelona, Spain

IN3, Open University of Catalonia, Barcelona, Spain
View Profile

,
Manuel Wimmer

CDL-MINT, Johannes Kepler University, Linz, Austria

CDL-MINT, Johannes Kepler University, Linz, Austria
View Profile

Authors Info & Claims

ACM Computing Surveys Volume 55 Issue 4Article No.: 72pp 1–39https://doi.org/10.1145/3523056

Published:21 November 2022Publication History

ACM Computing Surveys

Abstract

Model transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state-of-the-art. This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This article presents a survey on testing and debugging model transformations based on the analysis of 140 papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorize the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.

Supplemental Material

Available for Download

pdf

3523056.app.pdf (764.6 KB)

Supplementary appendix

REFERENCES

[1] Al-Sibahi A. S., Dimovski A. S., and Wąsowski A.. 2016. Symbolic execution of high-level transformations. In Proceedings of the SLE. 207–220.Google ScholarDigital Library
[2] Alkhazi B., Abid C., Kessentini M., Leroy D., and Wimmer M.. 2020. Multi-criteria test cases selection for model transformations. Autom. Softw. Eng. (2020), 91–118.Google ScholarDigital Library
[3] Almendros-Jiménez J. M. and Becerra-Terón A.. 2016. Automatic generation of Ecore models for testing ATL transformations. In Proceedings of the MEDI. 16–30.Google ScholarCross Ref
[4] Anastasakis K., Bordbar B., and Küster J.. 2007. Analysis of model transformations via Alloy. In Proceedings of the MoDeVVA. 47–56.Google Scholar
[5] Aranega V., Mottu J.-M., Etien A., Degueule T., Baudry B., and Dekeyser J.-L.. 2015. Towards an automation of the mutation analysis dedicated to model transformation. Softw. Test. Verif. Reliab. (2015), 653–683.Google ScholarDigital Library
[6] Aranega V., Mottu J.-M., Etien A., and Dekeyser J.. 2009. Traceability mechanism for error localization in model transformation. In Proceedings of the ICSOFT. 66–73.Google Scholar
[7] Aranega V., Mottu J.-M., Etien A., and Dekeyser J.. 2009. Using Trace to situate errors in model transformations. In Proceedings of the ICSOFT. 137–149.Google Scholar
[8] Aranega V., Mottu J.-M., Etien A., and Dekeyser J.. 2010. Using traceability to enhance mutation analysis dedicated to model transformation. In Proceedings of the MoDeVVA. 1–6.Google ScholarDigital Library
[9] Aranega V., Mottu J.-M., Etien A., and Dekeyser J.. 2011. Traceability for mutation analysis in model transformation. In Proceedings of the MODELS. 259–273.Google ScholarCross Ref
[10] Arifulina S., Soltenborn C., and Engels G.. 2012. Coverage criteria for testing DMM specifications. In Proceedings of the GTVMT.Google Scholar
[11] Batot E. and Sahraoui H.. 2016. A generic framework for model-set selection for the unification of testing and learning MDE tasks. In Proceedings of the MODELS. 374–384.Google ScholarDigital Library
[12] Bauer E. and Küster J. M.. 2011. Combining specification-based and code-based coverage for model transformation chains. In Proceedings of the ICMT. 78–92.Google ScholarCross Ref
[13] Bauer E., Küster J. M., and Engels G.. 2011. Test suite quality for model transformation chains. In Proceedings of the TOOLS. 3–19.Google ScholarCross Ref
[14] Bonfanti S., Gargantini A., and Mashkoor A.. 2020. Design and validation of a C++ code generator from abstract state machines specifications. J. Softw.: Evolut. Process 32 (2020), 1–27.Google ScholarDigital Library
[15] Braga C., Menezes R., Comicio T., Santos C., and Landim E.. 2011. On the specification, verification and implementation of model transformations with transformation contracts. In Proceedings of the SBMF. 108–123.Google ScholarDigital Library
[16] Braga C., Menezes R., Comicio T., Santos C., and Landim E.. 2012. Transformation contracts in practice. IET Softw. (2012), 16–32.Google ScholarCross Ref
[17] Brottier E., Fleurey F., Steel J., Baudry B., and Traon Y. L.. 2006. Metamodel-based test generation for model transformations: An algorithm and a tool. In Proceedings of the ISSRE. 85–94.Google ScholarDigital Library
[18] Burgueño L., Troya J., Wimmer M., and Vallecillo A.. 2015. Static fault localization in model transformations. IEEE Trans. Softw. Eng. (2015), 490–506.Google ScholarDigital Library
[19] Burgueño L., Wimmer M., and Vallecillo A.. 2012. Towards tracking “guilty” transformation rules: A requirements perspective. In Proceedings of the AMT. 27–32.Google ScholarDigital Library
[20] Burgueño L., Cabot J., Clarisó R., and Gogolla M.. 2019. A systematic approach to generate diverse instantiations for conceptual schemas. In Proceedings of the ER. 513–521.Google ScholarDigital Library
[21] Burgueño L., Hilken F., Vallecillo A., and Gogolla M.. 2016. Generating effective test suites for model transformations using classifying terms. In Proceedings of the PAME/VOLT. 48–57.Google Scholar
[22] Burgueño L., Hilken F., Vallecillo A., and Gogolla M.. 2017. Testing transformation models using classifying terms. In Proceedings of the ICMT. 69–85.Google ScholarCross Ref
[23] Calegari D. and Delgado A.. 2013. Rule chains coverage for testing QVT-Relations transformations. In Proceedings of the AMT. 1–10.Google Scholar
[24] Cariou E., Belloir N., Barbier F., and Djemam N.. 2009. OCL contracts for the verification of model transformations. In Proceedings of the OCL, Vol. 24. 1–15.Google Scholar
[25] Cariou E., Marvie R., Seinturier L., and Duchien L.. 2004. OCL for the specification of model transformation contracts. In Proceedings of the OCL-MDE. 1–15.Google Scholar
[26] Cheng Z. and Tisi M.. 2017. A deductive approach for fault localization in ATL model transformations. In Proceedings of the FASE. 300–317.Google ScholarDigital Library
[27] Cheng Z. and Tisi M.. 2018. Slicing ATL model transformations for scalable deductive verification and fault localization. Int. J. Softw. Tools Technol. Transf. (2018), 645–663.Google ScholarCross Ref
[28] Ciancone A., Filieri A., and Mirandola R.. 2010. MANTra: Towards model transformation testing. In Proceedings of the QUATIC. 97–105.Google ScholarDigital Library
[29] Ciancone A., Filieri A., and Mirandola R.. 2014. Testing operational transformations in model-driven engineering. Innov. Syst. Softw. Eng. (2014), 19–32.Google ScholarDigital Library
[30] Corley J.. 2014. Exploring omniscient debugging for model transformations. In Proceedings of the PSRC. 63–68.Google Scholar
[31] Corley J., Eddy B. P., Syriani E., and Gray J.. 2017. Efficient and scalable omniscient debugging for model transformations. Softw. Qual. J. (2017), 7–48.Google ScholarDigital Library
[32] Darabos A., Pataricza A., and Varró D.. 2008. Towards testing the implementation of graph transformations. In Proceedings of the GT-VMT. 75–85.Google ScholarDigital Library
[33] Dhoolia P., Mani S., Sinha V. S., and Sinha S.. 2010. Debugging model-transformation failures using dynamic tainting. In Proceedings of the ECOOP. 26–51.Google ScholarCross Ref
[34] Du K., Jiang M., Ding Z., Huang H., and Shu T.. 2020. Metamorphic testing in fault localization of model transformations. In Proceedings of the SOFL+MSVL. 299–314.Google ScholarDigital Library
[35] Ege F. and Tichy M.. 2019. A proposal of features to support analysis and debugging of declarative model transformations with graphical syntax by embedded visualizations. In Proceedings of the MODELS Companion. 326–330.Google ScholarDigital Library
[36] Ehrig K., Kuester J. M., and Taentzer G.. 2009. Generating instance models from meta models. Softw. Syst. Model. (2009), 479–500.Google ScholarCross Ref
[37] Ehrig K., Küster J. M., Taentzer G., and Winkelmann J.. 2006. Generating instance models from meta models. In Proceedings of the FMOODS. 156–170.Google ScholarDigital Library
[38] Finot O., Mottu J.-M., Sunyé G., and Attiogbé C.. 2013. Partial test oracle in model transformation testing. In Proceedings of the ICMT. 189–204.Google ScholarCross Ref
[39] Finot O., Mottu J.-M., Sunyé G., and Degueule T.. 2013. Using meta-model coverage to qualify test oracles. In Proceedings of the AMT. 1–10.Google Scholar
[40] Fiorentini C., Momigliano A., Ornaghi M., and Poernomo I.. 2010. A constructive approach to testing model transformations. In Proceedings of the ICMT. 77–92.Google ScholarCross Ref
[41] Fleurey F., Baudry B., Muller P., and Traon Y. Le. 2009. Qualifying input test data for model transformations. Softw. Syst. Model. (2009), 185–203.Google ScholarCross Ref
[42] Garcia J., Azanza M., Irastorza A., and Diaz O.. 2014. Testing MOFScript transformations with HandyMOF. In Proceedings of the ICMT. 42–56.Google ScholarCross Ref
[43] Gogolla M., Bohling J., and Richters M.. 2005. Validating UML and OCL models in USE by automatic snapshot generation. Softw. Syst. Model. (2005), 386–398.Google ScholarCross Ref
[44] Gogolla M. and Vallecillo A.. 2011. Tractable model transformation testing. In Proceedings of the ECMFA. 221–235.Google ScholarCross Ref
[45] Gogolla M., Vallecillo A., Burgueño L., and Hilken F.. 2017. Employing classifying terms for testing model transformations. In Proceedings of the MODELS. 312–321.Google Scholar
[46] Gómez-Abajo P., Guerra E., and Lara J. de. 2016. WODEL: A domain-specific language for model mutation. In Proceedings of the SAC. 1968–1973.Google ScholarDigital Library
[47] González C. A. and Cabot J.. 2012. ATLTest: A white-box test generation approach for ATL transformations. In Proceedings of the MODELS. 449–464.Google ScholarDigital Library
[48] Guerra E.. 2012. Specification-d test generation for model transformations. In Proceedings of the ICMT. 40–55.Google ScholarDigital Library
[49] Guerra E., Lara J. de, Kolovos D., and Paige R.. 2010. A visual specification language for model-to-model transformations. In Proceedings of the VLHCC. 119–126.Google ScholarDigital Library
[50] Guerra E., Lara J. de, Kolovos D. S., Paige R. F., and Santos O. M. dos. 2010. transML: A family of languages to model model transformations. In Proceedings of the MODELS. 106–120.Google ScholarCross Ref
[51] Guerra E., Lara J. de, Wimmer M., Kappel G., Kusel A., Retschitzegger W., Schoenboeck J., and Schwinger W.. 2013. Automated verification of model transformations based on visual contracts. Autom. Softw. Eng. (2013), 5–46.Google ScholarDigital Library
[52] Guerra E. and Soeken M.. 2015. Specification-driven model transformation testing. Softw. Syst. Model. (2015), 623–644.Google ScholarDigital Library
[53] Guerra E., Sánchez-Cuadrado J., and Lara J. de. 2019. Towards effective mutation testing for ATL. In Proceedings of the MODELS. 78–88.Google ScholarCross Ref
[54] Gómez-Abajo P., Guerra E., and Lara J. de. 2017. A domain-specific language for model mutation and its application to the automated generation of exercises. Comput. Lang. Syst. Struct. (2017), 152–173.Google Scholar
[55] He X., Chen X., Cai S., Zhang Y., and Huang G.. 2018. Testing bidirectional model transformation using metamorphic testing. Inf. Softw. Technol. (2018), 109–129.Google ScholarCross Ref
[56] He X., Li W., Zhang T., and Liu Y.. 2016. Towards parallel model generation for random performance testing of model-oriented operations. In Proceedings of the TASE. 57–64.Google ScholarCross Ref
[57] He X., Zhang T., Hu C., Ma Z., and Shao W.. 2016. An MDE performance testing framework based on random model generation. J. Syst. Softw. (2016), 247–264.Google ScholarDigital Library
[58] He X., Zhang T., Pan M., Ma Z., and Hu C.. 2019. Template-based model generation. Softw. Syst. Model. (2019), 2051–2092.Google ScholarDigital Library
[59] Heckel R., Khan T. A., and Machado R.. 2011. Towards test coverage criteria for visual contracts. In Proceedings of the GTVMT.Google Scholar
[60] Hibberd M., Lawley M., and Raymond K.. 2007. Forensic debugging of model transformations. In Proceedings of the MODELS. 589–604.Google ScholarCross Ref
[61] Hilken F., Gogolla M., Burgueño L., and Vallecillo A.. 2018. Testing models and model transformations using classifying terms. Softw. Syst. Model. (2018), 885–912.Google ScholarDigital Library
[62] Hue C. T. M., Hanh D. D., and Binh N. N.. 2018. A transformation-based method for test Case automatic generation from use cases. In Proceedings of the KSE. 252–257.Google ScholarCross Ref
[63] Jahanbin S. and Zamani B.. 2018. Test model generation using equivalence partitioning. In Proceedings of the ICCKE. 98–103.Google ScholarCross Ref
[64] Jiang M., Chen T. Y., Kuo F., Zhou Z., and Ding Z.. 2014. Testing model transformation programs using metamorphic testing. In Proceedings of the SEKE. 94–99.Google Scholar
[65] Jilani A. A., Iqbal M. Z., and Khan M. U.. 2014. A search-based test data generation approach for model transformations. In Proceedings of the ICMT. 17–24.Google ScholarCross Ref
[66] Jörges S. and Steffen B.. 2014. Back-to-back testing of model-based code generators. In Proceedings of the ISoLA. 425–444.Google ScholarDigital Library
[67] Jukss M., Verbrugge C., and Vangheluwe H.. 2017. Transformations debugging transformations. In Proceedings of the MODELS. 449–454.Google Scholar
[68] Kessentini M., Sahraoui H., and Boukadoum M.. 2010. Sequence diagram to colored petri nets transformation Testing: An immune system metaphor. In Proceedings of the CASCON. 72–85.Google ScholarDigital Library
[69] Kessentini M., Sahraoui H., and Boukadoum M.. 2011. Example-based model-transformation testing. Autom. Softw. Eng. (2011), 199–224.Google ScholarDigital Library
[70] Khan Y. and Hassine J.. 2013. Mutation operators for the Atlas transformation language. In Proceedings of the ICSTW Workshops. 43–52.Google ScholarDigital Library
[71] Kolovos D. S.. 2009. Establishing correspondences between models with the Epsilon comparison language. In Proceedings of the ECMDA-FA. 146–157.Google ScholarDigital Library
[72] Lahrouni M., Cariou E., and Fazziki A. E.. 2019. A black-box and contract-based verification of model transformations. Int. Arab J. Inf. Technol. (2019), 651–660.Google Scholar
[73] Lamari M.. 2007. Towards an automated test generation for the verification of model transformations. In Proceedings of the SAC. 998–1005.Google ScholarDigital Library
[74] Lengyel L. and Charaf H.. 2015. Test-driven verification/validation of model transformations. Front. Inf. Technol. Electron. Eng. (2015), 85–97.Google ScholarCross Ref
[75] Li P., Jiang M., and Ding Z.. 2020. Fault localization with weighted test model in model transformations. IEEE Access (2020), 14054–14064.Google ScholarCross Ref
[76] Lin Y., Zhang J., and Gray J.. 2005. A testing framework for model transformations. In Model Driven Software Development. 219–236.Google ScholarCross Ref
[77] Matragkas N. D., Kolovos D. S., Paige R. F., and Zolotas A.. 2013. A traceability-driven approach to model transformation testing. In Proceedings of the AMT. 1–10.Google Scholar
[78] Mazanek S. and Rutetzki C.. 2011. On the importance of model comparison tools for the automatic evaluation of the correctness of model transformations. In Proceedings of the IWMCP. 12–15.Google ScholarDigital Library
[79] McQuillan J. A. and Power J.. 2009. White-box coverage criteria for model transformations. In Proceedings of the AMT. 63–77.Google Scholar
[80] Mottu J.-M., Baudry B., and Traon Y. Le. 2006. Mutation analysis testing for model transformations. In Proceedings of the ECMDA-FA. 376–390.Google ScholarDigital Library
[81] Mottu J.-M., Baudry B., and Traon Y. L.. 2008. Model transformation testing: Oracle issue. In Proceedings of the MoDeVVA. 105–112.Google ScholarDigital Library
[82] Mottu J.-M., Sen S., Cadavid J., and Baudry B.. 2015. Discovering model transformation pre-conditions using automatically generated test models. In Proceedings of the ISSRE. 88–99.Google ScholarDigital Library
[83] Mottu J.-M., Sen S., Tisi M., and Cabot J.. 2012. Static analysis of model transformations for effective test generation. In Proceedings of the ISSRE. 291–300.Google ScholarDigital Library
[84] Mészáros T., Fehér P., and Lengyel L.. 2013. Visual debugging support for graph rewriting-based model transformations. In Proceedings of the EUROCON. 482–488.Google ScholarCross Ref
[85] Narayanan A. and Karsai G.. 2008. Verifying model transformations by structural correspondence. In Proceedings of the GT-VMT, Vol. 10. 1–15.Google Scholar
[86] Nassar N., Kosiol J., Kehrer T., and Taentzer G.. 2020. Generating large EMF models efficiently: A rule-based, configurable approach. In Proceedings of the FASE. 224–244.Google ScholarDigital Library
[87] Nguyen T. and Dang D.. 2018. An approach for testing model transformations. In Proceedings of the KSE. 264–269.Google ScholarCross Ref
[88] Nguyen T. H., Dang D. H., and Nguyen Q. T.. 2019. On analyzing rule dependencies to generate test cases for model transformations. In Proceedings of the KSE. 181–186.Google ScholarCross Ref
[89] Oakes B. J., Lucio L., Verbrugge C., and Vangheluwe H.. 2018. Debugging of model transformations and contracts in SyVOLT. In Proceedings of the MODELS Workshops. 532–537.Google Scholar
[90] Popoola S., Kolovos D. S., and Rodriguez H. H.. 2016. EMG: A domain-specific transformation language for synthetic model generation. In Proceedings of the ICMT. 36–51.Google ScholarDigital Library
[91] Rajeev A. C., Sampath P., Shashidhar K. C., and Ramesh S.. 2010. CoGenTe: A tool for code generator testing. In Proceedings of the ASE. 349–350.Google ScholarDigital Library
[92] Rodríguez-Echeverría R., Macías F., and Rutle A.. 2016. On reducing model transformation testing overhead. In Proceedings of the PAME/VOLT. 58–67.Google Scholar
[93] Rose L. M. and Poulding S.. 2013. Efficient probabilistic testing of model transformations using search. In Proceedings of the CMSBSE. 16–21.Google ScholarCross Ref
[94] Runge O., Khan T. A., and Heckel R.. 2013. Test case generation using visual contracts. In Proceedings of the GTVMT.Google Scholar
[95] Sahin D., Kessentini M., Wimmer M., and Deb K.. 2015. Model transformation testing: A bi-level search-based software engineering approach. J. Softw.: Evolut. Process (2015), 821–837.Google ScholarDigital Library
[96] Sampath P., Rajeev A. C., Ramesh S., and Shashidhar K. C.. 2008. Behaviour directed testing of auto-code generators. In Proceedings of the SEFM. 191–200.Google ScholarDigital Library
[97] Sánchez-Cuadrado J.. 2020. Towards interactive, test-driven development of model transformations. J. Object Technol. 19, 3 (2020), 1–12.Google Scholar
[98] Sánchez-Cuadrado J., Guerra E., and Lara J. de. 2015. Quick fixing ATL model transformations. In Proceedings of the MODELS. 146–155.Google ScholarCross Ref
[99] Sánchez-Cuadrado J., Guerra E., and Lara J. de. 2017. Static analysis of model transformations. IEEE Trans. Softw. Eng. (2017), 868–897.Google ScholarDigital Library
[100] Sánchez-Cuadrado J., Guerra E., and Lara J. de. 2018. Quick fixing ATL transformations with speculative analysis. Softw. Syst. Model. (2018), 779–813.Google ScholarDigital Library
[101] Scheidgen M.. 2015. Generation of large random models for benchmarking. In Proceedings of the BigMDE. 1–10.Google Scholar
[102] Schoenboeck J., Kappel G., Kusel A., Retschitzegger W., Schwinger W., and Wimmer M.. 2010. Catch me if you can—Debugging support for model transformations. In Proceedings of the MODELS. 5–20.Google ScholarDigital Library
[103] Schonbock J., Kappel G., Wimmer M., Kusel A., Retschitzegger W., and Schwinger W.. 2013. TETRABox—A generic white-box testing framework for model transformations. In Proceedings of the APSEC. 75–82.Google ScholarDigital Library
[104] Schönböck J., Kappel G., Wimmer M., Kusel A., Retschitzegger W., and Schwinger W.. 2012. Debugging model-to-model transformations. In Proceedings of the APSEC. 164–173.Google ScholarDigital Library
[105] Selim G. M. K., Büttner F., Cordy J. R., Dingel J., and Wang S.. 2013. Automated verification of model transformations in the automotive industry. In Proceedings of the MODELS. 690–706.Google ScholarDigital Library
[106] Semeráth O., Babikian A. A., Li A., Marussy K., and Varró D.. 2020. Automated generation of consistent models with structural and attribute constraints. In Proceedings of the MODELS. 187–199.Google ScholarDigital Library
[107] Semeráth O., Nagy A. S., and Varró D.. 2018. A graph solver for the automated generation of consistent domain-specific models. In Proceedings of the ICSE. 969–980.Google ScholarDigital Library
[108] Sen S. and Baudry B.. 2006. Mutation-based model synthesis in model driven engineering. In Proceedings of the Mutation Workshop. 1–10.Google ScholarDigital Library
[109] Sen S., Baudry B., and Mottu J.-M.. 2008. On combining multi-formalism knowledge to select models for model transformation testing. In Proceedings of the ICSE. 328–337.Google ScholarDigital Library
[110] Sen S., Baudry B., and Mottu J.-M.. 2009. Automatic model generation strategies for model transformation testing. In Proceedings of the ICMT. 148–164.Google ScholarDigital Library
[111] Sen S., Mottu J.-M., Tisi M., and Cabot J.. 2012. Using models of partial knowledge to test model transformations. In Proceedings of the ICMT. 24–39.Google ScholarDigital Library
[112] Shelburg J., Kessentini M., and Tauritz D. R.. 2013. Regression testing for model transformations: A multi-objective approach. In Proceedings of the SSBSE. 209–223.Google ScholarDigital Library
[113] Stuermer I., Conrad M., Doerr H., and Pepper P.. 2007. Systematic testing of model-based code generators. IEEE Trans. Softw. Eng. 33, 9 (2007), 622–634.Google ScholarDigital Library
[114] Sánchez-Cuadrado J., Guerra E., and Lara J. de. 2014. Uncovering errors in ATL model transformations using static analysis and constraint solving. In Proceedings of the ISSRE. 34–44.Google Scholar
[115] Sánchez-Cuadrado J., Guerra E., and Lara J. de. 2018. AnATLyzer: An advanced IDE for ATL model transformations. In Proceedings of the ICSE Companion. 85–88.Google Scholar
[116] Sánchez-Cuadrado J., Guerra E., Lara J. de, Clarisó R., and Cabot J.. 2017. Translating target to source constraints in model-to-model transformations. In Proceedings of the MODELS. 12–22.Google Scholar
[117] Tiso A., Reggio G., and Leotta M.. 2012. Early experiences on model transformation testing. In Proceedings of the AMT. 15–20.Google ScholarDigital Library
[118] Tiso A., Reggio G., and Leotta M.. 2013. A method for testing model to text transformations. In Proceedings of the AMT, Vol. 1077. 1–10.Google Scholar
[119] Tiso A., Reggio G., and Leotta M.. 2014. Unit testing of model to text transformations. In Proceedings of the AMT. 14–23.Google Scholar
[120] Troya J., Bergmayr A., Burgueño L., and Wimmer M.. 2015. Towards systematic mutations for and with ATL model transformations. In Proceedings of the ICSTW Workshops. 1–10.Google ScholarCross Ref
[121] Troya J., Segura S., Parejo J., and Ruiz-Cortés A.. 2017. An approach for debugging model transformations applying spectrum-based fault localization. In Proceedings of the JISBD. 1–4.Google Scholar
[122] Troya J., Segura S., Parejo J., and Ruiz-Cortés A.. 2018. Spectrum-based fault localization in model transformations. ACM Trans. Softw. Eng. Methodol. (2018), 1–50.Google ScholarDigital Library
[123] Troya J., Segura S., and Ruiz-Cortés A.. 2016. Towards the automation of metamorphic testing in model transformations. In Proceedings of the JISBD. 281–284.Google Scholar
[124] Troya J., Segura S., and Ruiz-Cortés A.. 2018. Automated inference of likely metamorphic relations for model transformations. J. Syst. Softw. (2018), 188–208.Google ScholarDigital Library
[125] Ujhelyi Z., Horváth, and Varró D.. 2011. Towards dynamic backward slicing of model transformations. In Proceedings of the ASE. 404–407.Google ScholarDigital Library
[126] Ujhelyi Z., Horváth, and Varró D.. 2012. Dynamic backward slicing of model transformations. In Proceedings of the ICST. 1–10.Google ScholarDigital Library
[127] Vallecillo A., Gogolla M., Burgueño L., Wimmer M., and Hamann L.. 2012. Formal specification and testing of model transformations. In Proceedings of the SMF. 399–437.Google ScholarDigital Library
[128] Wang J., Kim S., and Carrington D.. 2006. Verifying metamodel coverage of model transformations. In Proceedings of the ASWEC. 270–282.Google ScholarDigital Library
[129] Wang J., Kim S., and Carrington D.. 2008. Automatic generation of test models for model transformations. In Proceedings of the ASWEC. 432–440.Google ScholarCross Ref
[130] Wang W., Kessentini M., and Jiang W.. 2013. Test cases generation for model transformations from structural information. In Proceedings of the MDEBE. 42–51.Google Scholar
[131] Wieber M., Anjorin A., and Schürr A.. 2014. On the usage of TGGs for automated model transformation testing. In Proceedings of the ICMT. 1–16.Google ScholarCross Ref
[132] Wieber M. and Schürr A.. 2012. Gray box coverage criteria for testing graph pattern matching. In Proceedings of the GraBaTs, Vol. 52. 1–12.Google Scholar
[133] Wieber M. and Schürr A.. 2013. Systematic testing of graph transformations: A practical approach based on graph patterns. In Proceedings of the ICMT. 205–220.Google ScholarCross Ref
[134] Wimmer M. and Burgueño L.. 2013. Testing M2T/T2M transformations. In Proceedings of the MODELS. 203–219.Google ScholarDigital Library
[135] Wimmer M., Kappel G., Schoenboeck J., Kusel A., Retschitzegger W., and Schwinger W.. 2009. A petri net based debugging environment for QVT relations. In Proceedings of the ASE. 3–14.Google ScholarDigital Library
[136] Wimmer M., Kusel A., Reiter T., Retschitzegger W., Schwinger W., and Kappel G.. 2009. Lost in translation? Transformation nets to the rescue! In Proceedings of the UNISCON. 315–327.Google ScholarCross Ref
[137] Wimmer M., Kusel A., Schoenboeck J., Kappel G., Retschitzegger W., and Schwinger W.. 2009. Reviving QVT relations: Model-based debugging using colored petri nets. In Proceedings of the MODELS. 727–732.Google ScholarDigital Library
[138] Wu H.. 2016. Generating metamodel instances satisfying coverage criteria via SMT solving. In Proceedings of the MODELSWARD. 40–51.Google ScholarCross Ref
[139] Wu H., Monahan R., and Power J. F.. 2013. Exploiting attributed type graphs to generate metamodel instances using an SMT solver. In Proceedings of the TASE. 175–182.Google ScholarDigital Library
[140] Xiao H., Tian Z., Zhiyi M., and Weizhong S.. 2014. Randomized model generation for performance testing of model transformations. In Proceedings of the COMPSAC. 11–20.Google ScholarDigital Library
[141] [n.d.]. AnATLyzer. Retrieved from https://anatlyzer.github.io/.Google Scholar
[142] [n.d.]. ATL Zoo. Retrieved from http://www.eclipse.org/atl/atlTransformations.Google Scholar
[143] [n.d.]. DSLTrans. Retrieved from https://github.com/mbeddr/language_verification.Google Scholar
[144] [n.d.]. Eclipse Epsilon. Retrieved from https://www.eclipse.org/epsilon/.Google Scholar
[145] [n.d.]. EMF Model Generator. Retrieved from https://github.com/RuleBasedApproach/EMFModelGenerator.Google Scholar
[146] [n.d.]. EMFtoCSP. Retrieved from https://github.com/atlanmod/EMFtoCSP.Google Scholar
[147] [n.d.]. GEMOC Initiative. Retrieved from http://gemoc.org/.Google Scholar
[148] [n.d.]. MDE Testing. Retrieved from https://github.com/jdelara/MDETesting.Google Scholar
[149] [n.d.]. MRs4MTgenerator. Retrieved from https://gestionproyectos.us.es/projects/curso-ice-2016-rest-fp-coordinacioo/wiki.Google Scholar
[150] [n.d.]. PaMoMo. Retrieved from http://miso.es/tools/transML/main.htm.Google Scholar
[151] [n.d.]. PRAMANA. Retrieved from https://www.irisa.fr/triskell/Software/pramana/index.html.Google Scholar
[152] [n.d.]. RandomEMF. Retrieved from http://github.com/markus1978/RandomEMF.Google Scholar
[153] [n.d.]. SBFL_MT. Retrieved from https://github.com/javitroya/SBFL_MT.Google Scholar
[154] [n.d.]. SymexTRON. Retrieved from https://github.com/models-team/SymexTRON.Google Scholar
[155] [n.d.]. TracsTool. Retrieved from http://atenea.lcc.uma.es/projects/FaultLocMT.html.Google Scholar
[156] [n.d.]. Unnamed tool. Retrieved from http://atenea.lcc.uma.es/index.php/Main_Page/Resources/Mutations.Google Scholar
[157] [n.d.]. Unnamed tool. Retrieved from https://github.com/SOM-Research/constraint-mutation.Google Scholar
[158] [n.d.]. Unnamed tool. Retrieved from https://bitbucket.org/ustbmde/model-generation/wiki/Home.Google Scholar
[159] [n.d.]. USE. Retrieved from https://sourceforge.net/projects/useocl/.Google Scholar
[160] [n.d.]. VIATRA Generator. Retrieved from https://github.com/viatra/VIATRA-Generator.Google Scholar
[161] [n.d.]. WODEL. Retrieved from http://gomezabajo.github.io/Wodel/.Google Scholar
[162] Abade A., Ferrari F., and Lucrédio. D.2015. Testing M2T transformations—A systematic literature review. In Proceedings of the ICEIS. 177–187.Google ScholarDigital Library
[163] Addazi L., Cicchetti A., Rocco J. D., Ruscio D. D., Iovino L., and Pierantonio A.. 2016. Semantic-based model matching with EMFCompare. In Proceedings of the ME. 40–49.Google Scholar
[164] Amrani M., Combemale B., Lúcio L., Selim G., Dingel J., Traon Y. Le, Vangheluwe H., and Cordy J. R.. 2015. Formal verification techniques for model transformations: A tridimensional classification. J. Obj. Technol. (2015), 1:1–43.Google Scholar
[165] Arendt T., Biermann E., Jurack S., Krause C., and Taentzer G.. 2010. Henshin: Advanced concepts and tools for in-place EMF model transformations. In Proceedings of the MODELS. 121–135.Google ScholarCross Ref
[166] Barr E. T., Harman M., McMinn P., Shahbaz M., and Yoo S.. 2015. The oracle problem in software testing: A survey. IEEE Trans. Softw. Eng. 41, 5 (2015), 507–525.Google ScholarDigital Library
[167] Baudry B., Dinh-Trong T., Mottu J.-M., Simmonds D., France R., Ghosh S., Fleurey F., and Traon Y. Le. 2006. Model transformation testing challenges. In Proceedings of the IMDT. 1–10.Google Scholar
[168] Baudry B., Ghosh S., Fleurey F., France R., Traon Y. Le, and Mottu J.-M.. 2010. Barriers to systematic model transformation testing. Commun. ACM (2010), 139–143.Google ScholarDigital Library
[169] Benavides D., Segura S., and Cortés A. R.. 2010. Automated analysis of feature models 20 years later: A literature review. Inf. Syst. 35, 6 (2010), 615–636.Google ScholarDigital Library
[170] Benouda H., Azizi M., Esbai R., and Moussaoui M.. 2016. Code generation approach for mobile application using acceleo. Int. Rev. Comput. Softw. (2016), 160–166.Google Scholar
[171] Bézivin J., Rumpe B., Schürr A., and Tratt L.. 2005. Model transformations in practice workshop. In Proceedings of the MODELS Satellite Events. 120–127.Google Scholar
[172] Birken K.. 2014. Building code generators for DSLs using a partial evaluator for the Xtend language. In Proceedings of the ISOLA. 407–424.Google ScholarDigital Library
[173] Bousse E., Wimmer M., Schwinger W., and Kapsammer E.. 2016. On leveraging executable language engineering for domain-specific transformation languages. In Proceedings of the EM. 41–43.Google Scholar
[174] Brambilla M., Cabot J., and Wimmer M.. 2017. Model-Driven Software Engineering in Practice (2nd edition).Google ScholarDigital Library
[175] Bruneliere H., Cabot J., Jouault F., and Madiot F.. 2010. MoDisco: A generic and extensible framework for model driven reverse engineering. In Proceedings of the ASE. 173–174.Google ScholarDigital Library
[176] Burgueño L., Cabot J., and Gérard S.. 2019. The future of model transformation languages: An open community discussion. J. Obj. Technol.7 (2019), 1–11.Google Scholar
[177] Büttner F., Egea M., Cabot J., and Gogolla M.. 2012. Verification of ATL transformations using transformation models and model finders. In Proceedings of the ICFEM. 198–213.Google ScholarDigital Library
[178] Bézivin J., Rumpe B., Schürr A., and Tratt L.. 2005. Model transformations in practice workshop. In Proceedings of the MODELS Satellite Events. 120–127.Google Scholar
[179] Cabot J., Clarisó R., and Riera D.. 2014. On the verification of UML/OCL class diagrams using constraint programming. J. Syst. Softw. 93 (2014), 1–23.Google ScholarCross Ref
[180] Cabot J. and Gogolla M.. 2012. Object constraint language (OCL): A definitive guide. In Proceedings of the SFM. 58–90.Google ScholarDigital Library
[181] Cadavid J. J., Combemale B., and Baudry B.. 2015. An analysis of metamodeling practices for MOF and OCL. Comput. Lang., Syst. Struct. (2015), 42–65.Google Scholar
[182] Calegari D. and Szasz N.. 2013. Verification of model transformations: A survey of the state-of-the-art. Electron. Notes Theoret. Comput. Sci. 292 (2013), 5–25.Google ScholarDigital Library
[183] Chen J., Patra J., Pradel M., Xiong Y., Zhang H., Hao D., and Zhang L.. 2020. A survey of compiler testing. ACM Comput. Surv. 53, 1 (2020), 4:1–4:36.Google Scholar
[184] Chen T. Y., Kuo F., Liu H., Poon P., Towey D., Tse T. H., and Zhou Z. Q.. 2018. Metamorphic testing: A review of challenges and opportunities. Comput. Surv. 51, 1 (2018), 4:1–4:27.Google Scholar
[185] Cicchetti A., Ruscio D. Di, Eramo R., and Pierantonio A.. 2011. JTL: A bidirectional and change propagating transformation language. In Proceedings of the SLE. 183–202.Google ScholarCross Ref
[186] Ciccozzi F., Malavolta I., and Selic B.. 2019. Execution of UML models: A systematic review of research and practice. Softw. Syst. Model. (2019), 2313–2360.Google ScholarDigital Library
[187] Clavel M., Durán F., Eker S., Lincoln P., Martí-Oliet N., Meseguer J., and Talcott C.. 2007. All About Maude—A High-Performance Logical Framework.Google ScholarCross Ref
[188] Csertán G., Huszerl G., Majzik I., Pap Z., Pataricza A., and Varró D.. 2002. VIATRA—Visual automated transformations for formal verification and validation of UML models. In Proceedings of the ASE. 267–270.Google ScholarCross Ref
[189] Czarnecki K. and Helsen S.. 2006. Feature-based survey of model transformation approaches. IBM Syst. J. (2006), 621–646.Google ScholarDigital Library
[190] Silva A. R. da. 2015. Model-driven engineering: A survey supported by the unified conceptual model. Comput. Lang., Syst. Struct. (2015), 139–155.Google Scholar
[191] Lara J. de and Vangheluwe H.. 2002. AToM3: A tool for multi-formalism and meta-modelling. In Proceedings of the FASE, Vol. 2306. 174–188.Google ScholarCross Ref
[192] Moura L. de and Bjørner N.. 2008. Z3: An efficient SMT solver. In Proceedings of the TACAS. 337–340.Google ScholarCross Ref
[193] Deb K., Pratap A., Agarwal S., and Meyarivan T.. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Computat. (2002), 182–197.Google ScholarDigital Library
[194] Ehrig H., Ehrig K., Lara J. de, Taentzer G., Varró D., and Varró-Gyapay S.. 2005. Termination criteria for model transformation. In Proceedings of the FASE. 49–63.Google ScholarDigital Library
[195] Ehrig H., Engels G., Kreowski H.-J., and Rozenberg G. (Eds.). 1999. Handbook of Graph Grammars and Computing by Graph Transformation: Vol. 2: Applications, Languages, and Tools.Google Scholar
[196] Fleck M., Troya J., and Wimmer M.. 2015. Marrying search-based optimization and model transformation technology. In Proceedings of the NasBASE. 1–16.Google Scholar
[197] Fleck M., Troya J., and Wimmer M.. 2016. Search-based model transformations. J. Softw.: Evolut. Process (2016), 1081–1117.Google ScholarDigital Library
[198] Fraser G. and Rojas J. M.. 2019. Software testing. In Handbook of Software Engineering. 123–192.Google ScholarCross Ref
[199] Gammaitoni L., Kelsen P., and Ma Q.. 2018. Agile validation of model transformations using compound F-Alloy specifications. Sci. Comput. Program. (2018), 55–75.Google ScholarCross Ref
[200] Garousi V., Felderer M., and Mäntylä M. V.. 2019. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. Inf. Softw. Technol. 106 (2019), 101–121.Google ScholarCross Ref
[201] Gogolla M., Büttner F., and Richters M.. 2007. USE: A UML-based specification environment for validating UML and OCL. Sci. Comput. Program. (2007), 27–34.Google ScholarDigital Library
[202] González C. A. and Cabot J.. 2014. Formal verification of static software models in MDE: A systematic review. Inf. Softw. Technol. (2014), 821–838.Google ScholarCross Ref
[203] Greenyer J. and Kindler E.. 2010. Comparing relational model transformation technologies: Implementing query/view/transformation with triple graph grammars. Softw. Syst. Model. (2010), 21–46.Google ScholarCross Ref
[204] Groner R., Beaucamp L., Tichy M., and Becker S.. 2020. An exploratory study on performance engineering in model transformations. In Proceedings of the MODELS. 308–319.Google ScholarDigital Library
[205] Groner R., Gylstorff S., and Tichy M.. 2020. A Profiler for the Matching Process of Henshin.Google ScholarDigital Library
[206] Guerra E., Lara J. de, Kolovos D., and Paige R.. 2010. A visual specification language for model-to-model transformations. In Proceedings of the VLHCC. 119–126.Google ScholarDigital Library
[207] Heckel R. and Taentzer G.. 2020. Graph Transformation for Software Engineers—With Applications to Model-Based Development and Domain-Specific Language Engineering.Google ScholarCross Ref
[208] Hettab A., Kerkouche E., and Chaoui A.. 2015. A graph transformation approach for automatic test cases generation from UML activity diagrams. In Proceedings of the C3S2E. 88–97.Google Scholar
[209] Ishibuchi H., Tsukamoto N., and Nojima Y.. 2008. Evolutionary many-objective optimization. In Proceedings of the GEFS. 47–52.Google ScholarCross Ref
[210] ISO 29119-4 2015. ISO/IEC/IEEE International Standard - Software and systems engineering–Software testing–Part 1: Concepts and Definitions. Standard.Google Scholar
[211] ISO/IEC/IEEE. 2013. ISO/IEC/IEEE international standard - software and systems engineering –software testing –part 1:Concepts and definitions. ISO/IEC/IEEE 29119-1:2013(E) (2013), 1–64. DOI:Google ScholarCross Ref
[212] Jackson D.. 2002. Alloy: A lightweight object modelling notation. ACM Trans. Softw. Eng. and Methodology (2002), 256–290.Google ScholarDigital Library
[213] Javed A. Z., Strooper P. A., and Watson G. N.. 2007. Automated Generation of Test Cases Using Model-Driven Architecture. In Proceedings of the AST. 3–3.Google ScholarDigital Library
[214] Jézéquel J., Barais O., and Fleurey F.. 2011. Model driven language engineering with Kermeta. In Proceedings of the GTTSE. 201–221.Google Scholar
[215] Jia Y. and Harman M.. 2011. An analysis and survey of the development of mutation testing. IEEE Trans. Softw. Eng. (2011), 649–678.Google ScholarDigital Library
[216] Jouault F., Allilaire F., Bézivin J., Kurtev I., and Valduriez P.. 2006. ATL: A QVT-like transformation language. In Proceedings of the OOPSLA Companion. 719–720.Google ScholarDigital Library
[217] Kahani N., Bagherzadeh M., Cordy J. R., Dingel J., and Varró D.. 2019. Survey and classification of model transformation tools. Softw. Syst. Model. (2019), 2361–2397.Google ScholarDigital Library
[218] Kalaee A. and Rafe V.. 2019. Model-based test suite generation for graph transformation system using model simulation and search-based techniques. Inf. Softw. Technol. (2019), 1–29.Google ScholarCross Ref
[219] Khorram F., Bousse E., Mottu J., and Sunyé G.. 2021. Adapting TDL to provide testing support for executable DSLs. J. Obj. Technol. 20, 3 (2021), 6:1–15.Google Scholar
[220] Kitchenham B.. 2004. Procedures for Performing Systematic Reviews.Joint Technical Report, Keele University TR/SE-0401 and NICTA 0400011T.1.Google Scholar
[221] Kleppe A., Warmer J., and Cook S.. 1999. Informal formality? The object constraint language and its application in the UML metamodel. In Proceedings of the UML. 148–161.Google ScholarCross Ref
[222] Kühne T.. 2006. Matters of (meta-) modeling. Softw. Syst. Model. (2006), 369–385.Google ScholarCross Ref
[223] Lano K., Kolahdouz-Rahimi S., Yassipour-Tehrani S., and Sharbaf M.. 2018. A survey of model transformation design patterns in practice. J. Syst. Softw. (2018), 48–73.Google ScholarCross Ref
[224] Lawley M., Duddy K., Gerber A., and Raymond K.. 2004. Language features for re-use and maintainability of MDA transformations. In Proceedings of the OOPSLA Workshops.Google Scholar
[225] Lúcio L., Amrani M., Dingel J., Lambers L., Salay R., Selim G., Syriani E., and Wimmer M.. 2014. Model transformation intents and their properties. Softw. Syst. Model. (2014), 1–35.Google Scholar
[226] Ludewig J.. 2003. Models in software engineering—An introduction. Softw. Syst. Model. (2003), 5–14.Google ScholarCross Ref
[227] Mannadiar R.. 2012. A Multi-paradigm Modelling Approach to the Foundations of Domain-specific Modelling. Ph.D. Dissertation. MgGill University.Google Scholar
[228] McMinn P.. 2004. Search-based software test data generation: A survey. Softw. Test., Verif. Reliab. 14, 2 (2004), 105–156.Google ScholarCross Ref
[229] Mellor S. J., Scott K., Uhl A., Weise D., and Soley R. M.. 2004. MDA Distilled: Principles of Model-driven Architecture.Google ScholarDigital Library
[230] Mens T. and Gorp P. V.. 2006. A taxonomy of model transformation. Electron. Notes Theoret. Comput. Sci. (2006), 125–142.Google ScholarDigital Library
[231] Oakes B. J., Troya J., Lúcio L., and Wimmer M.. 2018. Full contract verification for ATL using symbolic execution. Softw. Syst. Model. (2018), 815–849.Google ScholarDigital Library
[232] Offutt J. and Xu W.. 2004. Generating test cases for web services using data perturbation. SIGSOFT Softw. Eng. Notes 29, 5 (2004), 1–10. Google ScholarDigital Library
[233] Oldevik J., Neple T., Grønmo R., Aagedal J., and Berre A.. 2005. Toward standardised model to text transformations. In Proceedings of the ECMFA. 239–253.Google ScholarDigital Library
[234] Poulding S. and Clark J. A.. 2010. Efficient software verification: Statistical testing using automated search. IEEE Trans. Softw. Eng. (2010), 763–777.Google ScholarDigital Library
[235] Rahim L. A. and Whittle J.. 2015. A survey of approaches for verifying model transformations. Softw. Syst. Model. (2015), 1003–1028.Google ScholarDigital Library
[236] Rivera J. E., Duran F., and Vallecillo A.. 2009. A graphical approach for modeling time-dependent behavior of DSLs. In Proceedings of the VL/HCC. 51–55.Google ScholarDigital Library
[237] Rose L. M., Matragkas N., Kolovos D. S., and Paige R. F.. 2012. A feature model for model-to-text transformation languages. In Proceedings of the MiSE. 57–63.Google ScholarCross Ref
[238] Rose L. M., Paige R. F., Kolovos D. S., and Polack F. A.. 2008. The Epsilon Generation Language. In Proceedings of the ECMFA. 1–16.Google ScholarDigital Library
[239] Sahin D., Kessentini M., Bechikh S., and Deb K.. 2014. Code-smell detection as a bilevel problem. ACM Trans. Softw. Eng. Methodol. 24, 6 (2014), 1–44. Issue 1.Google ScholarDigital Library
[240] Cuadrado J. Sanchez, Burgueno L., Wimmer M., and Vallecillo A.. 2020. Efficient execution of ATL model transformations using static analysis and parallelism. IEEE Trans. Softw. Eng. (2020).Google Scholar
[241] Schürr A.. 1995. Specification of graph translators with triple graph grammars. In Proceedings of the WG. 151–163.Google ScholarCross Ref
[242] Segura S., Fraser G., Sánchez A., and Ruiz-Cortes A.. 2016. A survey on metamorphic testing. IEEE Trans. Softw. Eng. (2016), 805–824.Google ScholarCross Ref
[243] Segura S., Towey D., Zhou Z. Q., and Chen T. Y.. 2020. Metamorphic testing: Testing the untestable. IEEE Softw. (2020), 46–53.Google ScholarCross Ref
[244] Selim G. M. K., Cordy J. R., and Dingel J.. 2012. Model transformation testing: The state of the art. In Proceedings of the AMT. 21–26.Google ScholarDigital Library
[245] Selim G. M. K., Cordy J. R., Dingel J., Lucio L., and Oakes B. J.. 2015. Finding and fixing bugs in model transformations with formal verification: An experience report. In Proceedings of the AMT. 26–35.Google Scholar
[246] Sendall S. and Kozaczynski W.. 2003. Model transformation: The heart and soul of model-driven software development. IEEE Softw. (2003), 42–45.Google ScholarDigital Library
[247] Shan L. and Zhu H.. 2009. Generating structurally complex test cases by data mutation: A case study of testing an automated modelling tool. Comput. J. 52, 5 (2009), 571–588.Google ScholarDigital Library
[248] Stevens P.. 2007. A landscape of bidirectional model transformations. In Proceedings of the GTTSE. 408–424.Google Scholar
[249] Taentzer G.. 2003. AGG: A graph transformation environment for modeling and validation of software. In Proceedings of the AGTIVE. 446–453.Google Scholar
[250] Tisi M., Jouault F., Fraternali P., Ceri S., and Bézivin J.. 2009. On the use of higher-order model transformations. In Proceedings of the ECMDA-FA. 18–33.Google ScholarDigital Library
[251] Tisi M., Perez S. M., and Choura H.. 2013. Parallel execution of ATL transformation rules. In Proceedings of the MODELS. 656–672.Google ScholarDigital Library
[252] Torlak E. and Jackson D.. 2007. Kodkod: A relational model finder. In Proceedings of the TACAS. 632–647.Google ScholarCross Ref
[253] Troya J., Moreno N., Bertoa M. F., and Vallecillo A.. 2021. Uncertainty representation in software models: A survey. Softw. Syst. Model. (2021), 1–31.Google Scholar
[254] Troya J., Segura S., Burgueño L., and Wimmer M.. 2021. Model transformation testing and debugging: A survey. Retrieved from http://atenea.lcc.uma.es/projects/MTTestingDebuggingSurvey.html.Google Scholar
[255] Troya J. and Vallecillo A.. 2011. A rewriting logic semantics for ATL. J. Obj. Technol. (2011), 5:1–29.Google Scholar
[256] Varró D. and Pataricza A.. 2003. Automated formal verification of model transformations. In Proceedings of the CSDUML. 63–78.Google Scholar
[257] Webster J. and Watson R. T.. 2002. Analyzing the past to prepare for the future: Writing a literature review. MIS Quart. 26, 2 (2002).Google Scholar
[258] Weyuker E. J.. 1982. On testing non-testable programs. Comput. J. (1982), 465–470.Google ScholarCross Ref
[259] Wohlin C.. 2014. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the EASE.Google ScholarDigital Library
[260] Wong W. E., Gao R., Li Y., Abreu R., and Wotawa F.. 2016. A survey on software fault localization. IEEE Trans. Softw. Eng. 42, 8 (2016), 707–740.Google ScholarDigital Library
[261] Yoo S. and Harman M.. 2012. Regression testing minimization, selection and prioritization: A survey. Softw. Test., Verif. Reliab. (2012), 67–120.Google ScholarDigital Library
[262] Yousefian R., Rafe V., and Rahmani M.. 2014. A heuristic solution for model checking graph transformation systems. Appl. Softw. Comput. (2014), 169–180.Google ScholarDigital Library
[263] Zhu H., Hall P. A. V., and May J. H. R.. 1997. Software unit test coverage and adequacy. Comput. Surv. 29, 4 (1997), 366–427.Google ScholarDigital Library

Index Terms

Model Transformation Testing and Debugging: A Survey
1. Software and its engineering

Recommendations

Model transformation by-example: a survey of the first wave
Conceptual Modelling and Its Theoretical Foundations

Model-Driven Engineering (MDE) places models as first-class artifacts throughout the software lifecycle. In this context, model transformations are crucial for the success of MDE, being comparable in role and importance to compilers for high-level ...
Read More
A model transformation approach to automatic model construction and evolution
ASE '05: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering

As models are elevated to first-class artifacts within the software development lifecycle, the task of construction and evolution of large-scale system models becomes a manually intensive effort that can be very time consuming and error prone. To ...
Read More
Transformation techniques in the model-driven development process of UWE
ICWE '06: Workshop proceedings of the sixth international conference on Web engineering

Development of Web software is still an inefficient and error-prone process. We need integrated techniques and tool support for automated generation of Web systems. The goal of model-driven development (MDD) is to tackle these problems introducing a ...
Read More

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Published in
ACM Computing Surveys Volume 55, Issue 4
April 2023
871 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3567469
Editor:
Albert Zomaya
University of Sydney, Australia
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
- Published: 21 November 2022
- Online AM: 16 March 2022
- Accepted: 28 February 2022
- Revised: 24 February 2022
- Received: 31 May 2021
Published in csur Volume 55, Issue 4

Permissions
Request permissions about this article.
Request Permissions

Check for updates
Author Tags
Model transformation
testing
debugging
survey
Qualifiers
- survey
- Refereed
Conference
Funding Sources
Other Metrics
View Article Metrics

Article Metrics
- 1
  Total Citations
  View Citations
- 893
  Total Downloads
- Downloads (Last 12 months)285
- Downloads (Last 6 weeks)31
Other Metrics
View Author Metrics
Cited By
View all

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

View Full Text

HTML Format

View this article in HTML Format .

View HTML Format

Model Transformation Testing and Debugging: A Survey

ACM Computing Surveys

Abstract

Supplemental Material

Available for Download

REFERENCES

Cited By

Index Terms

Recommendations

Model transformation by-example: a survey of the first wave

A model transformation approach to automatic model construction and evolution

Transformation techniques in the model-driven development process of UWE