The multiple sequence alignment problem in biology
SIAM Journal on Applied Mathematics
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fitness Function Design To Improve Evolutionary Structural Testing
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Proceedings of the 17th IEEE international conference on Automated software engineering
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Evolutionary testing in the presence of loop-assigned flags: a testability transformation approach
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Model-Based Test Driven Development of the Tefkat Model-Transformation Engine
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Model comparison: a foundation for model composition and model transformation testing
Proceedings of the 2006 international workshop on Global integrated model management
Metamodel-based Test Generation for Model Transformations: an Algorithm and a Tool
ISSRE '06 Proceedings of the 17th International Symposium on Software Reliability Engineering
Automating model transformation by example using inductive logic programming
Proceedings of the 2007 ACM symposium on Applied computing
Model-driven Development of Complex Software: A Research Roadmap
FOSE '07 2007 Future of Software Engineering
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Towards Model Transformation Generation By-Example
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Testing Model-Processing Tools for Embedded Systems
RTAS '07 Proceedings of the 13th IEEE Real Time and Embedded Technology and Applications Symposium
Software defect prediction using artificial immune recognition system
SE'07 Proceedings of the 25th conference on IASTED International Multi-Conference: Software Engineering
Model Transformation as an Optimization Problem
MoDELS '08 Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems
Model transformation testing: oracle issue
ICSTW '08 Proceedings of the 2008 IEEE International Conference on Software Testing Verification and Validation Workshop
Model Transformation by Demonstration
MODELS '09 Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems
Validation of model transformations: first experiences using a white box approach
MoDELS'06 Proceedings of the 2006 international conference on Models in software engineering
Mutation analysis testing for model transformations
ECMDA-FA'06 Proceedings of the Second European conference on Model Driven Architecture: foundations and Applications
Search-based model transformation by example
Software and Systems Modeling (SoSyM)
Artificial neural networks as multi-networks automated test oracle
Automated Software Engineering
Automated verification of model transformations based on visual contracts
Automated Software Engineering
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A major concern in model-driven engineering is how to ensure the quality of the model-transformation mechanisms. One validation method that is commonly used is model transformation testing. When using this method, two important issues need to 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 the latter. We propose a novel oracle function for model transformation testing that relies on the premise that the more a transformation deviates from well-known good transformation examples, the more likely it is erroneous. More precisely, the proposed oracle function compares target test cases with a base of examples that contains good quality transformation traces, and then assigns a risk level to them accordingly. Our approach takes inspiration from the biological metaphor of immune systems, where pathogens are identified by their difference with normal body cells. A significant feature of the approach is that one no longer needs to define an expected model for each test case. Furthermore, the detected faulty candidates are ordered by degree of risk, which helps the tester inspect the results. The validation results on a transformation mechanism used by an industrial partner confirm the effectiveness of our approach.