@inproceedings {28419,
	title = {Automating Test Oracle Generation in DevOps for Industrial Elevators},
	journal = {29th IEEE International Conference on Software Analysis, Evolution and Reengineering},
	year = {2022},
	pages = {284-288},
	publisher = { IEEE},
	doi = {10.1109/SANER53432.2022.00044},
	author = {Arrieta, Aitor and Otaegi, Maialen and Han, Liping and Sagardui, Goiuria and Ali, Shaukat and Arratibel, Maite},
	editor = {Ciolkowski, Marcus and Palomba, Fabio}
}
@inproceedings {27929,
	title = {Genetic Algorithm-based Testing of Industrial Elevators under Passenger Uncertainty},
	journal = {The 32nd International Symposium on Software Reliability Engineering (ISSRE 2021)},
	year = {2021},
	publisher = { IEEE},
	author = {Galarraga, Joritz and Arrieta, Aitor and Ali, Shaukat and Sagardui, Goiuria and Arratibel, Maite}
}
@inproceedings {27888,
	title = {Microservices for Continuous Deployment, Monitoring and Validation in Cyber-Physical Systems: an Industrial Case Study for Elevators Systems},
	journal = {ICSA Software Architecture in Practice (SAIP) },
	year = {2021},
	pages = {46-53},
	publisher = { IEEE},
	author = {Gartziandia, Aitor and Arrieta, Aitor and Ali, Shaukat and Yue, Tao and Agirre, Aitor and Sagardui, Goiuria and Arratibel, Maite}
}
@article {25695,
	title = {Employing Multi-Objective Search to Enhance Reactive Test Case Generation and Prioritization for Testing Industrial Cyber Physical Systems},
	journal = {IEEE Transactions on Industrial Informatics (TII)},
	volume = {14},
	year = {2018},
	pages = {1055-1066},
	publisher = {IEEE},
	abstract = {The test case generation and prioritization of industrial Cyber-Physical Systems (CPSs) face critical challenges and simulation-based testing is one of the most commonly used techniques for testing these complex systems. However, simulation models of industrial CPSs are usually very complex and executing the simulations becomes computationally expensive, which often make it infeasible to execute all the test cases. To address these challenges, this paper proposes a multi-objective test generation and prioritization approach for testing industrial CPSs by defining a fitness function with four objectives and designing different crossover and mutation operators. We empirically evaluated our fitness function and designed operators along with five multi-objective search algorithms (e.g., Non-dominated Sorting Genetic Algorithm (NSGA-II)) using four case studies. The evaluation results demonstrated that NSGA-II achieved significantly better performance than the other algorithms and managed to improve Random Search for on average 43.80\% for each objective and 49.25\% for the quality indicator Hypervolume (HV).\&nbsp;},
	doi = {10.1109/TII.2017.2788019},
	url = {http://ieeexplore.ieee.org/abstract/document/8241845/},
	author = {Arrieta, Aitor and Wang, Shuai and Markiegi, Urtzi and Sagardui, Goiuria and Etxeberria, Leire}
}
@inproceedings {26076,
	title = {Model- Based Personalized Visualization System for Monitoring Evolving Industrial Cyber-Physical System},
	journal = {The 25th Asia-Pacific Software Engineering Conference (APSEC 2018) },
	year = {2018},
	publisher = {IEEE},
	author = {Iglesias, Aitziber and Yue, Tao and Arellano, Cristobal and Ali, Shaukat and Sagardui, Goiuria}
}
@inproceedings {25414,
	title = {Product Line Engineering of Monitoring Functionality in Industrial Cyber-Physical Systems: A Domain Analysis},
	journal = {The 21st International Systems and Software Product Line Conference},
	year = {2017},
	publisher = {ACM},
	author = {Iglesias, Aitziber and Lu, Hong and Arellano, Cristobal and Yue, Tao and Ali, Shaukat},
	editor = {Sagardui, Goiuria}
}
@inproceedings {25250,
	title = {Search-Based Test Case Generation for Cyber-Physical Systems},
	journal = {IEEE Congress on Evolutionary Computation (CEC)},
	year = {2017},
	month = {06/2017},
	pages = {688-697},
	publisher = {IEEE},
	abstract = {The test case generation of Cyber-Physical Systems (CPSs) face critical challenges that traditional methods such as Model-Based Testing cannot deal with. As a result, simulation- based testing is one of the most commonly used techniques for testing CPSs despite sometimes being computationally too expensive. This paper proposes a search-based approach which is implemented on top of Non-dominated Sorting Genetic Algorithm II (NSGA-II), the most commonly applied multi-objective search algorithm for cost-effectively generating executable test cases in order to test CPSs. With the aim of guiding the generation of the optimal set of so-called reactive test cases, the approach formally defines three cost-effectiveness measures: requirements coverage, test case similarity and test execution time. Furthermore, we design one crossover operator and three mutation operators (i.e., mutation at test suite level named Mu TS, mutation at test case level named Mu TC and mutation at both levels named Mu BO) for test case generation. We evaluate our approach by comparing with Random Search (RS) using four study subjects (one of them is an industrial system). Moreover, we evaluate the three mutation operators using the four study subjects. The results of the experiment (with a rigorous statistical analysis) indicated that our approach in conjunction with the crossover operator operation and three mutation operators significantly outperformed RS. In general, Mu BO achieved the best performance among the three mutation operators and managed to improve on average the test execution time by 14\%, the requirements coverage by 34\%, and the test similarity by 75\% as compared with RS.\&nbsp;},
	doi = {10.1109/CEC.2017.7969377},
	author = {Arrieta, Aitor and Wang, Shuai and Markiegi, Urtzi and Sagardui, Goiuria},
	editor = {Etxeberria, Leire}
}
@inproceedings {24529,
	title = {Search-Based Test Case Selection of Cyber-Physical System Product Lines for Simulation-Based Validation},
	journal = {International Systems and Software Product Line Conference (SPLC)},
	year = {2016},
	pages = {297-306},
	author = {Arrieta, Aitor and Wang, Shuai and Sagardui, Goiuria and Etxeberria, Leire}
}
@inproceedings {24468,
	title = {Test Case Prioritization of Configurable Cyber-Physical Systems with Weight-Based Search Algorithms},
	journal = {Genetic and Evolutionary Computation Conference (GECCO)},
	year = {2016},
	month = {07/2016},
	pages = {1053-1060},
	abstract = {Cyber-Physical Systems (CPSs) can be found in many sectors (e.g., automotive and aerospace). These systems are usually configurable to give solutions based on different needs. The variability of these systems is large, which implies they can be set into millions of configurations. As a result, different testing processes are needed to efficiently test these systems: the appropriate configurations must be selected and relevant test cases for each configuration must be chosen as well as prioritized. Prioritizing the order in which the test cases are executed reduces the time for detecting faults in these kinds of systems. However, the test suite size is often large and exploring all the possible test case orders is infeasible. Search algorithms can help find an optimal solution from a large solution space. This paper presents an approach based on weight-based search algorithms for prioritizing the test cases for configurable CPSs. We empirically analyze the performance of the following algorithms with two case studies: Weight-Based Genetic Algorithms, Randomly Weighted Genetic Algorithms, Greedy, Alternating Variable Method and Random Search (RS). Our results suggest that all the search algorithms outperform RS, which is taken as a baseline. Local search algorithms have shown better performance than global search algorithms.},
	author = {Arrieta, Aitor and Wang, Shuai and Sagardui, Goiuria and Etxeberria, Leire}
}
