A methodology for controlling the size of a test suite
ACM Transactions on Software Engineering and Methodology (TOSEM)
Multivariate visualization in observation-based testing
Proceedings of the 22nd international conference on Software engineering
Computer
VIATRA " Visual Automated Transformations for Formal Verification and Validation of UML Models
Proceedings of the 17th IEEE international conference on Automated software engineering
An Empirical Study of the Effects of Minimization on the Fault Detection Capabilities of Test Suites
ICSM '98 Proceedings of the International Conference on Software Maintenance
Test-Suite Reduction for Model Based Tests: Effects on Test Quality and Implications for Testing
Proceedings of the 19th IEEE international conference on Automated software engineering
Feature-based survey of model transformation approaches
IBM Systems Journal - Model-driven software development
Introduction to Software Testing
Introduction to Software Testing
ATL: A model transformation tool
Science of Computer Programming
Constructing and Visualizing Transformation Chains
ECMDA-FA '08 Proceedings of the 4th European conference on Model Driven Architecture: Foundations and Applications
Detecting and Resolving Process Model Differences in the Absence of a Change Log
BPM '08 Proceedings of the 6th International Conference on Business Process Management
A Dynamic Test Cluster Sampling Strategy by Leveraging Execution Spectra Information
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
A Visual Specification Language for Model-to-Model Transformations
VLHCC '10 Proceedings of the 2010 IEEE Symposium on Visual Languages and Human-Centric Computing
Model transformation testing: the state of the art
Proceedings of the First Workshop on the Analysis of Model Transformations
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For testing model transformations or model transformation chains, a software engineer usually designs a test suite consisting of test cases where each test case consists of one or several models. In order to ensure a high quality of such a test suite, coverage achieved by the test cases with regards to the system under test must be systematically measured. Using coverage analysis and the resulting coverage information, missing test cases and redundant test cases can be identified and thereby the quality of the test suite can be improved. As test cases consist of models, a coverage analysis approach must measure how complete models cover the domains of the transformations in the chain and to what degree of completeness transformations are covered when executing the test suite. In this paper, we present a coverage analysis approach for measuring test suite quality for model transformation chains. Our approach combines different coverage criteria and yields detailed coverage information that can be used to identify missing and redundant test cases.