Title

Fixing Model Transformation Errors Using Heuristic Search

Presenter Information

Nathan Barron

Department

Computer Science

Major

Computer Science

Research Advisor

Kessentini, Marouane

Advisor's Department

Computer Science

Funding Source

Missouri S& T Opportunities for Undergraduate Research Experiences (OURE) Program

Abstract

One of the efficient techniques used in model-driven engineering to ensure the quality of model-transformation mechanisms is testing. Two important steps should be addressed: the efficient generation/selection of test cases and the definition of oracle functions that assess the validity of the transformed models. This work is concerned with an additional step related to automatically fixing model transformation errors. In this work, we propose an evolutionary approach that uses good traceability links to fix detected transformation errors. This novel evolutionary approach is based on the dissimilarity between the new collected traceability links after fixing errors and the base of good traceability links. Thus, the best solution is the set of changes (on transformation rules or metamodels) that maximize the similarity between our base of good traceability links and the new ones collected after executing the changes to correct errors. The validation results on widely-used transformation mechanism confirm the effectiveness of our approach in terms of precision and recall on ten different scenarios.

Biography

Nathan is presently a junior at Missouri University of Science and Technology. Nathan is getting a BS currently in computer science and a minor in mathematics. Nathan interests include: graphical user interface, volunteer work, racquetball, soccer, and many more outdoor activities.

Research Category

Sciences

Presentation Type

Oral Presentation

Document Type

Presentation

Location

Upper Atrium/Hallway

Presentation Date

03 Apr 2013, 9:00 am - 11:45 am

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Apr 3rd, 9:00 AM Apr 3rd, 11:45 AM

Fixing Model Transformation Errors Using Heuristic Search

Upper Atrium/Hallway

One of the efficient techniques used in model-driven engineering to ensure the quality of model-transformation mechanisms is testing. Two important steps should be addressed: the efficient generation/selection of test cases and the definition of oracle functions that assess the validity of the transformed models. This work is concerned with an additional step related to automatically fixing model transformation errors. In this work, we propose an evolutionary approach that uses good traceability links to fix detected transformation errors. This novel evolutionary approach is based on the dissimilarity between the new collected traceability links after fixing errors and the base of good traceability links. Thus, the best solution is the set of changes (on transformation rules or metamodels) that maximize the similarity between our base of good traceability links and the new ones collected after executing the changes to correct errors. The validation results on widely-used transformation mechanism confirm the effectiveness of our approach in terms of precision and recall on ten different scenarios.