@inproceedings {Simula.approve.16,
	title = {Black-Box System Testing of Real-Time Embedded Systems Using Random and Search-Based Testing},
	journal = {22nd IFIP International Conference on Testing Software and Systems (ICTSS) - formerly TestCom/FATES},
	volume = {6435},
	year = {2010},
	publisher = {Springer},
	type = {Conference},
	abstract = {Testing real-time embedded systems (RTES) is in many ways challenging. Thousands of test cases can be potentially executed on an industrial RTES. Given the magnitude of testing at the system level, only a fully automated approach can really scale up to test industrial RTES. In this paper we take a black-box approach and model the RTES environment using the UML/- MARTE international standard. Our main motivation is to provide a more practical approach to the model-based testing of RTES by allowing system testers, who are often not familiar with the system design but know the application domain well-enough, to model the environment to enable test automation. Environment models can support the automation of three tasks: the code generation of an environment simulator, the selection of test cases, and the evaluation of their expected results (oracles). In this paper, we focus on the second task (test case selection) and investigate three test automation strategies using inputs from UML/MARTE environment models: Random Testing (baseline), Adaptive Random Testing, and Search-Based Testing (using Genetic Algorithms). Based on one industrial case study and three artificial systems, we show how, in general, no technique is generally better than the other. Which test selection technique to use is determined by the failure rate (testing stage) and the execution time of test cases. Finally, we propose a practical process to combine the use of all three test strategies.},
	isbn = {978-3-642-16572-6},
	author = {Arcuri, Andrea and Muhammad Zohaib {Iqbal} and Briand, Lionel},
	editor = {Petrenko, A. and Sim{\~a}o, A. and Maldonado, J.C.}
}
@inproceedings {Simula.approve.34,
	title = {Reducing the Cost of Model-Based Testing Through Test Case Diversity},
	journal = {22nd IFIP International Conference on Testing Software and Systems (ICTSS) - formerly TestCom/FATES},
	volume = {6435},
	year = {2010},
	publisher = {Springer},
	type = {Conference},
	abstract = {Model-based testing (MBT) suffers from two main problems which in many real world systems make MBT impractical: scalability and automatic oracle generation. When no automated oracle is available, or when testing must be performed on actual hardware or a restricted-access network, for example, only a small set of test cases can be executed and evaluated. However, MBT techniques usually generate large sets of test cases when applied to real systems, regardless of the coverage criteria. Therefore, one needs to select a small enough subset of these test cases that have the highest possible fault revealing power. In this paper, we investigate and compare various techniques for rewarding diversity in the selected test cases as a way to increase the likelihood of fault detection. We use a similarity measure defined on the representation of the test cases and use it in several algorithms that aim at maximizing the diversity of test cases. Using an industrial system with actual faults, we found that rewarding diversity leads to higher fault detection compared to the techniques commonly reported in the literature: coverage-based and random selection. Among the investigated algorithms, diversification using Genetic Algorithms is the most cost-effective technique.},
	isbn = {978-3-642-16572-6},
	author = {Hemmati, Hadi and Arcuri, Andrea and Briand, Lionel},
	editor = {Petrenko, A. and Sim{\~a}o, A. and Maldonado, J.C.}
}
