Готові списки джерел за темами / TEST SUIT OPTIMIZATION

Добірка наукової літератури з теми "TEST SUIT OPTIMIZATION"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "TEST SUIT OPTIMIZATION"

Mohapatra, Sudhir Kumar, and Srinivas Prasad. "Test Case Reduction Using Ant Colony Optimization for Object Oriented Program." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (December 1, 2015): 1424. http://dx.doi.org/10.11591/ijece.v5i6.pp1424-1432.

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Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization.

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Johari, Nur Farahlina, Azlan Mohd Zain, Noorfa Haszlinna Mustaffa, and Amirmudin Udin. "Optimization of Surface Roughness in Turning Operation Using Firefly Algorithm." Applied Mechanics and Materials 815 (November 2015): 268–72. http://dx.doi.org/10.4028/www.scientific.net/amm.815.268.

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Recently, Firefly Algorithm (FA) has become an important technique to solve optimization problems. Various FA variants have been developed to suit various applications. In this paper, FA is used to optimize machining parameters such as % Volume fraction of SiC (V), cutting speed (S), feed rate (F), depth of cut (D) and machining time (T). The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

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Liu, Yong, Wei-Min Zheng, Shangkun Liu, and Qing-Wei Chai. "Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks." Entropy 24, no. 8 (August 12, 2022): 1109. http://dx.doi.org/10.3390/e24081109.

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Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.

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Liu, Jinguo, Yifan Luo, and Zhaojie Ju. "An Interactive Astronaut-Robot System with Gesture Control." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7845102.

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Human-robot interaction (HRI) plays an important role in future planetary exploration mission, where astronauts with extravehicular activities (EVA) have to communicate with robot assistants by speech-type or gesture-type user interfaces embedded in their space suits. This paper presents an interactive astronaut-robot system integrating a data-glove with a space suit for the astronaut to use hand gestures to control a snake-like robot. Support vector machine (SVM) is employed to recognize hand gestures and particle swarm optimization (PSO) algorithm is used to optimize the parameters of SVM to further improve its recognition accuracy. Various hand gestures from American Sign Language (ASL) have been selected and used to test and validate the performance of the proposed system.

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Koay, Ying-Ying, Jian-Ding Tan, Chin-Wai Lim, Siaw-Paw Koh, Sieh-Kiong Tiong, and Kharudin Ali. "An adaptive gravitational search algorithm for global optimization." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (November 1, 2019): 724. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp724-729.

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<span>Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.</span>

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You, Qi, Jun Sun, Vasile Palade, and Feng Pan. "Quantum-behaved particle swarm optimization with dynamic grouping searching strategy." Intelligent Data Analysis 27, no. 3 (May 18, 2023): 769–89. http://dx.doi.org/10.3233/ida-226753.

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The quantum-behaved particle swarm optimization (QPSO) algorithm, a variant of particle swarm optimization (PSO), has been proven to be an effective tool to solve various of optimization problems. However, like other PSO variants, it often suffers a premature convergence, especially when solving complex optimization problems. Considering this issue, this paper proposes a hybrid QPSO with dynamic grouping searching strategy, named QPSO-DGS. During the search process, the particle swarm is dynamically grouped into two subpopulations, which are assigned to implement the exploration and exploitation search, respectively. In each subpopulation, a comprehensive learning strategy is used for each particle to adjust its personal best position with a certain probability. Besides, a modified opposition-based computation is employed to improve the swarm diversity. The experimental comparison is conducted between the QPSO-DGS and other seven state-of-art PSO variants on the CEC’2013 test suit. The experimental results show that QPSO-DGS has a promising performance in terms of the solution accuracy and the convergence speed on the majority of these test functions, and especially on multimodal problems.

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Ajani, Oladayo S., Abhishek Kumar, Rammohan Mallipeddi, Swagatam Das, and Ponnuthurai Nagaratnam Suganthan. "Benchmarking Optimization-Based Energy Disaggregation Algorithms." Energies 15, no. 5 (February 22, 2022): 1600. http://dx.doi.org/10.3390/en15051600.

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Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED.

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Chai, Qing-Wei, and Jerry Wangtao Zheng. "Rotated Black Hole: A New Heuristic Optimization for Reducing Localization Error of WSN in 3D Terrain." Wireless Communications and Mobile Computing 2021 (October 11, 2021): 1–13. http://dx.doi.org/10.1155/2021/9255810.

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Wireless sensor network (WSN) attracts the attention of more and more researchers, and it is applied in more and more environment. The localization information is one of the most important information in WSN. This paper proposed a novel algorithm called the rotated black hole (RBH) algorithm, which introduces a rotated optimal path and greatly improves the global search ability of the original black hole (BH) algorithm. Then, the novel algorithm is applied in reducing the localization error of WSN in 3D terrain. CEC 2013 test suit is used to verify the performance of the novel algorithm, and the simulation results show that the novel algorithm has better search performance than other famous intelligence computing algorithms. The localization simulation experiment results reveal that the novel algorithm also has an excellent performance in solving practical problems. WSN localization 3D terrain intelligence computing rotated the black hole algorithm.

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Puphasuk, Pikul, and Jeerayut Wetweerapong. "An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization." Foundations of Computing and Decision Sciences 45, no. 2 (June 1, 2020): 97–124. http://dx.doi.org/10.2478/fcds-2020-0007.

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AbstractDesigning an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.

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Chen, Z. J., Z. J. Cheng, and X. Q. Yan. "Multiobjective Optimization Problem of Multireservoir System in Semiarid Areas." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/354206.

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With the increasing scarcity of water resources, the growing importance of the optimization operation of the multireservoir system in water resources development, utilization, and management is increasingly evident. Some of the existing optimization methods are inadequate in applicability and effectiveness. Therefore, we need further research in how to enhance the applicability and effectiveness of the algorithm. On the basis of the research of the multireservoir system’s operating parameters in the Urumqi River basin, we establish a multiobjective optimization problem (MOP) model of water resources development, which meets the requirements of water resources development. In the mathematical model, the domestic water consumption is the biggest, the production of industry and agricultural is the largest, the gross output value of industry and agricultural is the highest, and the investment of the water development is the minimum. We use the weighted variable-step shuffled frog leaping algorithm (SFLA) to resolve it, which satisfies the constraints. Through establishing the test function and performance metrics, we deduce the evolutionary algorithms, which suit for solving MOP of the scheduling, and realize the multiobjective optimization of the multireservoir system. After that, using the fuzzy theory, we convert the competitive multiobjective function into single objective problem of maximum satisfaction, which is the only solution. A feasible solution is provided to resolve the multiobjective scheduling optimization of multireservoir system in the Urumqi River basin. It is the significance of the layout of production, the regional protection of ecological environment, and the sufficient and rational use of natural resources, in Urumqi and the surrounding areas.

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Більше джерел

Дисертації з теми "TEST SUIT OPTIMIZATION"

Kallio, R. (Roope). "Towards test suite optimization in software component testing by finding and analysing repeats in test trace." Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201505291729.

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Regression testing is often a very resource and time consuming activity with large scale software. However, regression tests may be run in continuous integration where everything should run fast in order to get quick feedback. This thesis reviews the existing solutions to optimize regression test suites and test suites in general by looking at various existing heuristic, computational intelligence and other methods. In most cases, these methods are based on the fact that test suites may contain redundant test cases. They also require code coverage, fault detection capability or requirement coverage information of each test case in order to be implemented. Additionally, the thesis studies ways to detect redundancy in test trace. It is also discussed whether or not this kind of redundancy information can be used for test suite optimization instead. A new concept, pair-wise supermaximal repeats is presented. A case study is conducted in software component continuous integration testing environment in Nokia Networks. Main methods include looking for patterns in test trace manually in a plot representing the test trace as well as finding pair-wise supermaximal repeats in the test trace with an enhanced suffix array implementation developed in test driven development method. A new application was developed, MRS Finder. The repeats are then analyzed. The study shows that the there can be found huge amount of repetition which makes analyzing all of it hard by hand. The repeats also tend to occur more in the setup or teardown phase of a test case rather than in the action or assertion phase of the test cases. The study shows that MRS Finder offers different optimization possibilities than originally was thought
Regressiotestaus on usein hyvin resursseja ja aikaa kuluttavaa suurten ohjelmistojen kanssa. Regressiotestejä voidaan kuitenkin ajaa jatkuvassa integroinnissa, jossa senkin tulisi suoriutua nopeasti, jotta palauteaika pysyy lyhyenä. Tämä diplomityö käy läpi olemassa olevia ratkaisuja regressiotestien ja yleisestikin testisarjojen optimointia. Näistä käsitellään muun muassa heuristisia ja laskennallisen älykkyyden menetelmiä. Useimmissa tapauksissa nämä metodit perustuvat siihen, että testisarjat voivat sisältää testitapauksia, joissa on keskinäistä redundanssia. Menetelmät vaativat myös tietoa jokaisen testitapauksen koodikattavuudesta, virheiden paljastuskyvystä tai vaatimuskattavuudesta, jotta ne voidaan toteuttaa käytännössä. Tämä työ tutkii myös tapoja löytää redundanssia testien jäljistä. Työ pohtii, voitaisiinko tätä tietoa käyttää testisarjojen optimointiin edellä mainittujen tietojen sijaan. Tämä työ esittelee uuden käsitteen, parittaisen supermaksimaalisen toiston. Tapaustutkimus suoritettiin Nokia Networksin systeemikomponenttitason jatkuvan integroinnin järjestelmässä. Tapaustutkimuksessa kehitettiin testivetoisella kehityksellä MRS Finder -ohjelmisto, joka etsii toistoja parannetusta suffiksitaulusta ja tuottaa diagrammin, joka edustaa testien jälkiä. Diagrammista etsitään toistoja manuaalisesti ja suffiksitaulusta löydettyjä toistoja analysoidaan. Tutkimus näyttää, että testien jäljistä voidaan löytää suuri määrä toistoja, joka tekee analysoinnista vaikeaa käsin. Toistot esiintyvät useammin testi tapauksien alkuasetus (setup) ja poiskorjaus (teardown) -vaiheissa kuin toiminta- tai vakuutusvaiheissa (assertion). Tutkimus näyttää myös, että MRS Finderilla löydetään erilaisia testi tapauksien optimointimahdollisuuksia kuin aluksi ajateltiin

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Fong, Fredric, and Mustafa Raed. "Performance comparison of GraalVM, Oracle JDK andOpenJDK for optimization of test suite execution time." Thesis, Mittuniversitetet, Institutionen för data- och systemvetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-43169.

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Testing, when done correctly, is an important part of software development sinceit is a measure of the quality of a software in question. Most of the highly ratedsoftware projects therefore have test suites implemented that include unit tests,integration tests, and other types of tests. However, a challenge regarding the testsuite is that it needs to run each time new code changes are proposed. From thedeveloper’s perspective, it might not always be necessary to run the whole testsuite for small code changes. Previous studies have tried to tackle this probleme.g., by only running a subset of the test suite. This research investigates runningthe whole test suite of Java projects faster, by testing the Java Development Kits(JDKs) GraalVM Enterprise Edition (EE) and Community Edition (CE) againstOracle JDK and OpenJDK for Java 8 and 11. The research used the test suiteexecution time as a metric to compare the JDKs. Another metric that wasconsidered was the test suites number of test cases, used to try and find a breakingpoint for when GraalVM becomes beneficial. The tests were performed on twotest machines, where the first used 20 out of 48 tested projects and the secondused 11 out of 43 projects tested. When looking at the average of five runs,GraalVM EE 11 performed best in 11 out of 18 projects on the first test machine,compared to its closest competitor, and in 7 out of 11 projects on the second testmachine both for JDK 8 and 11. However GraalVM EE 8 did not give anybenefits to the first test machine compared to its competitors, which might indicatethat the hardware plays a vital role in the performance of GraalVM EE 8. Numberof test cases could not be used to determine a breaking point for when GraalVM isbeneficial, but it was observed that GraalVM did not show any benefits forprojects with an execution time of fewer than 39 seconds. It is observed thatGraalVM CE, does not perform well as compared to the other JDKs, and in allcases, its performance is not countable due to less non-satisfied and inefficientbehavior.

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Pugsley, Thomas S. "Characterization, optimization, and test of the NPSATI MEMS 3-axis sensor suite for use in small satellite attitude control." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Sep%5FPugsley.pdf.

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Thesis (M.S. in Space Systems Operations)--Naval Postgraduate School, September 2007.
Thesis Advisor(s): Newman, James H. "September 2007." Description based on title screen as viewed on October 25, 2007. Includes bibliographical references (p.151-153). Also available in print.

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GUPTA, VISHAL. "TEST SUITE OPTIMIZATION USING NATURE BASED METAHEURISTIC APPROACH." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15068.

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Regression testing is a type of software testing that intends to validate modified software and it confirms that modifications made to the software have no adverse side effects. The goal of regression testing is to validate the modified software. Due to the resource and time constraints, it becomes necessary to develop techniques to minimize existing test suites by eliminating redundant test cases and prioritizing them. Running all test cases in an existing test suite can consume an inordinate amount of time and resources. Thus, it is necessary to select the minimum set of test cases from existing test suite with the ability to cover all the faults in minimum execution time. When analyzing large test suits, redundancies are identified in test cases, hence, it is necessary to reduce these suites, in order to fit the available resources, without severely compromising the coverage of the test adequacy criterion being observed. Test case prioritization techniques intend to arrange test cases of a test suite in a way, with the goal of maximizing some objective function. There are various classical techniques of test case prioritization. In this research we proposed a nature inspired meta-heuristic approach for test prioritization.

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Частини книг з теми "TEST SUIT OPTIMIZATION"

Ding, Ke, and Ying Tan. "cuROB: A GPU-Based Test Suit for Real-Parameter Optimization." In Lecture Notes in Computer Science, 66–78. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11897-0_9.

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Asthana, Manish, Kapil Dev Gupta, and Arvind Kumar. "Test Suite Optimization Using Lion Search Algorithm." In Advances in Intelligent Systems and Computing, 77–90. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1518-7_7.

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Dalal, Renu, Manju Khari, Tushar Singh Bhal, and Kunal Sharma. "NGA-II-Based Test Suite Minimization in Software." In Optimization of Automated Software Testing Using Meta-Heuristic Techniques, 1–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07297-0_1.

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Carlsson, Mats, Arnaud Gotlieb, and Dusica Marijan. "Software Product Line Test Suite Reduction with Constraint Optimization." In Communications in Computer and Information Science, 68–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62569-0_4.

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Dincǎ, Ionuţ. "Multi-objective Test Suite Optimization for Event-B Models." In Informatics Engineering and Information Science, 551–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25327-0_47.

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Shao, Chang, Qi Zhao, Yuhui Shi, and Jing Jiang. "Generalized Test Suite for Continuous Dynamic Multi-objective Optimization." In Lecture Notes in Computer Science, 205–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72062-9_17.

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Johnson, Victoria, João A. Duro, Visakan Kadirkamanathan, and Robin C. Purshouse. "A Scalable Test Suite for Bi-objective Multidisciplinary Optimization." In Lecture Notes in Computer Science, 319–32. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_23.

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Kenny, Angus, Tapabrata Ray, Hemant Kumar Singh, and Xiaodong Li. "A Test Suite for Multi-objective Multi-fidelity Optimization." In Lecture Notes in Computer Science, 361–73. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_26.

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Sheng, Yunlong, Chang’an Wei, and Shouda Jiang. "Particle Swarm Optimization Based Parallel Input Time Test Suite Construction." In Advances in Intelligent Systems and Computing, 29–36. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5841-8_4.

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Mohanty, Subhasish, Sudhir Kumar Mohapatra, and Sultan Feisso Meko. "Ant Colony Optimization (ACO-Min) Algorithm for Test Suite Minimization." In Advances in Intelligent Systems and Computing, 55–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_6.

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Тези доповідей конференцій з теми "TEST SUIT OPTIMIZATION"

Edholm, P., A. Wahl, H. Johannesson, and R. So¨derberg. "Knowledge-Based Configuration of Integrated Product and Process Platforms." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86540.

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The definition of the content of a product platform highly affects the possibility to engineer and produce unique and optimized products to suit customer needs. In this paper, generic configurable autonomous subsystems, Configurable Components (CCs), are used to define platforms that can be used to configure and instantiate product as well as process structures and to optimize instantiated part solutions. To simulate a realistic industrial environment, a configuration demonstrator has been developed and used to perform case studies in order to test the CC concept. The test cases are focused on geometrical interfaces between components. Communication functionality between the demonstrator and a CAT (Computer Aided Tolerancing) tool has been developed to enable automatic optimization of interface concepts during configuration. In summary, the paper shows that, given variant-specific input data, a knowledge-based platform definition with high design bandwidth can be used to configure, engineer and manufacture an instanced product variant.

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Krömmer, W., and P. Heinrich. "Development of Coatings and the Influence of Industrial Gases on the Thermal Spray Process." In ITSC2008, edited by B. R. Marple, M. M. Hyland, Y. C. Lau, C. J. Li, R. S. Lima, and G. Montavon. Verlag für Schweißen und verwandte Verfahren DVS-Verlag GmbH, 2008. http://dx.doi.org/10.31399/asm.cp.itsc2008p0065.

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Abstract Equipment engineering, materials, gases and know-how are integral to the smooth operation of any system. The user often focuses on the attributes of the individual systems, but it is only in conjunction with all their components that the merits of the process can be exploited to the full. Here, industrial gases serve as essential building blocks which positively affect the process in an endless variety of ways. The optimization of costs, extended lifetime of systems and tailoring of coating properties to suit specific applications are just some of these influencing variables. Gas producers test countless facilities and thermal spray systems in their own laboratories and are therefore always in a position to provide the right solution for existing and new applications.

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Krömmer, W., and P. Heinrich. "Selective Impact of Industrial Gases on the Thermal Spray Process." In ITSC2010, edited by B. R. Marple, A. Agarwal, M. M. Hyland, Y. C. Lau, C. J. Li, R. S. Lima, and G. Montavon. DVS Media GmbH, 2010. http://dx.doi.org/10.31399/asm.cp.itsc2010p0228.

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Abstract The trend in thermal spraying is more and more towards a globally uniform level of high-grade spray coatings. It is therefore extremely important that auxiliaries such as spray materials or industrial gases undergo precise examination in order to exactly define their influence. A further point is of particular significance in today’s business world. With ever-increasing raw material prices, it is absolutely essential that spray processes are optimized to the maximum. This is not only made possible by selecting the right system, but also by choosing the right gas and gas mixture. The optimization of costs, extended lifetime of systems and tailoring of coating properties to suit specific applications are just some of these influencing variables. Gas producers test countless facilities and thermal spray systems in their own laboratories and are therefore always in a position to provide the right solution for existing and new applications.

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M. Ali, Ahmed, and Dirk Söffker. "Realtime Application of Progressive Optimal Search and Adaptive Dynamic Programming in Multi-Source HEVs." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5081.

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In multi-source hybrid electric vehicles (HEVs), more degrees of freedom are introduced to the power management optimization problem. Formulation of the optimal control problem and selection of solution method significantly affect the realtime applicability of the developed strategy. Moreover, elaborate definition of objective cost function and control variables are the essences to approach the global optimal solution in real-time. In this paper, two novel developed concepts are introduced: Adaptive Dynamic Programming (ADP) and Progressive Optimal Search (POS). The problem definition and solution method are structured in each method to yield minimum fuel consumption and suit realtime applicability. The results are comparatively analyzed with respect to previous developed methods of rule-based (RB) and adaptive RB. Experimental application of the developed methods is conducted using a hardware-in-the-loop (HIL) test-rig to validate the control modules. The comparative analysis emphasizes the advantages of each method from the perspectives of trip cost minimization, charge sustenance, and realtime applicability.

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Zhang, Xiaofang, Huamao Shan, and Ju Qian. "Resource-Aware Test Suite Optimization." In 2009 9th International Conference on Quality Software (QSIC 2009). IEEE, 2009. http://dx.doi.org/10.1109/qsic.2009.51.

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Li, Ding, Cagri Sahin, James Clause, and William G. J. Halfond. "Energy-directed test suite optimization." In 2013 2nd International Workshop on Green and Sustainable Software (GREENS). IEEE, 2013. http://dx.doi.org/10.1109/greens.2013.6606423.

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Ansari, Ahlam S. A., Kailas K. Devadkar, and Prachi Gharpure. "Optimization of test suite-test case in regression test." In 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2013. http://dx.doi.org/10.1109/iccic.2013.6724206.

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Padula, S., N. Alexandrov, and L. Green. "MDO test suite at NASA Langley Research Center." In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4028.

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Li, Ding, Yuchen Jin, Cagri Sahin, James Clause, and William G. J. Halfond. "Integrated energy-directed test suite optimization." In the 2014 International Symposium. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2610384.2610414.

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Haider, Aftab Ali, Shahzad Rafiq, and Aamer Nadeem. "Test suite optimization using fuzzy logic." In 2012 International Conference on Emerging Technologies (ICET). IEEE, 2012. http://dx.doi.org/10.1109/icet.2012.6375440.

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