Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Constraint-Based Automatic Test Data Generation
IEEE Transactions on Software Engineering
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Software unit test coverage and adequacy
ACM Computing Surveys (CSUR)
Automated program flaw finding using simulated annealing
Proceedings of the 1998 ACM SIGSOFT international symposium on Software testing and analysis
The dynamic domain reduction procedure for test data generation
Software—Practice & Experience
Automated test-data generation for exception conditions
Software—Practice & Experience
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Improving Evolutionary Testing By Flag Removal
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An Automated Framework for Structural Test-Data Generation
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Fully Automatic Worst-Case Execution Time Analysis for Matlab/Simulink Models
ECRTS '02 Proceedings of the 14th Euromicro Conference on Real-Time Systems
Testing the Results of Static Worst-Case Execution-Time Analysis
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Search-based mutation testing for Simulink models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Predicate expression cost functions to guide evolutionary search for test data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
The state problem for evolutionary testing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A search-based framework for automatic testing of MATLAB/Simulink models
Journal of Systems and Software
Dealing with inheritance in OO evolutionary testing
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Comparing algorithms for search-based test data generation of matlab® simulink® models
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Transition coverage testing for simulink/stateflow models using messy genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolutionary algorithm for prioritized pairwise test data generation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Multi-objective coevolutionary automated software correction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Search-based system testing: high coverage, no false alarms
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Evolutionary algorithms for the multi-objective test data generation problem
Software—Practice & Experience
Analysis and testing of matlab simulink models: a systematic mapping study
Proceedings of the 2013 International Workshop on Joining AcadeMiA and Industry Contributions to testing Automation
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Search based test-data generation has proved successful for code-level testing. In this paper we investigate the application of such approaches at the higher levels of abstraction offered by Matlab-Simulink models. The presence of persistent state has been shown to be problematic at the code level and such difficulties remain when Matlab-Simulink models are to be tested. In such cases, sequences of inputs that can put the model under test into particular states are needed to enable the underlying test goals to be achieved. Simple search guidance appears to be insufficient and results in a 'flat' cost function landscape. To address this problem, we introduce a technique called tracing and deducing, which helps provide better guidance to the search, allowing our developed tools to home in on the targeted test-data.