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Evolutionary functional black-box testing in an industrial setting

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Evolutionary functional black-box testing in an industrial setting

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dc.contributor.author Vos ., Tanja Ernestina es_ES
dc.contributor.author Lindlar, Felix F. es_ES
dc.contributor.author Wilmes, Benjamin es_ES
dc.contributor.author Windisch, Andreas es_ES
dc.contributor.author Baars, Arthur Iwan es_ES
dc.contributor.author Kruse, Peter M. es_ES
dc.contributor.author Gross, Hamilton es_ES
dc.contributor.author Wegener, Joachim es_ES
dc.date.accessioned 2014-12-17T18:41:02Z
dc.date.available 2014-12-17T18:41:02Z
dc.date.issued 2013-06
dc.identifier.issn 0963-9314
dc.identifier.uri http://hdl.handle.net/10251/45571
dc.description.abstract During the past years, evolutionary testing research has reported encouraging results for automated functional (i.e. black-box) testing. However, despite promising results, these techniques have hardly been applied to complex, real-world systems and as such, little is known about their scalability, applicability, and acceptability in industry. In this paper, we describe the empirical setup used to study the use of evolutionary functional testing in industry through two case studies, drawn from serial production development environments at Daimler and Berner & Mattner Systemtechnik, respectively. Results of the case studies are presented, and research questions are assessed based on them. In summary, the results indicate that evolutionary functional testing in an industrial setting is both scalable and applicable. However, the creation of fitness functions is time-consuming. Although in some cases, this is compensated by the results, it is still a significant factor preventing functional evolutionary testing from more widespread use in industry. es_ES
dc.description.sponsorship This work is supported by EU grant IST-33472 (EvoTest). For their support and help, we would like to thank Mark Harman, Kiran Lakhotia and Youssef Hassoun from Kings College London; Marc Schoenauer and Luis da Costa from INRIA; Jochen Hansel from Fraunhofer FIRST; Dimitar Dimitrov and Ivaylo Spasov from RILA; and Dimitris Togias from European Dynamics. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Software Quality Journal es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Evolutionary computation es_ES
dc.subject Functional testing es_ES
dc.subject Empirical assessment es_ES
dc.subject Case study es_ES
dc.subject Industrial practice es_ES
dc.subject Test data generation es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Evolutionary functional black-box testing in an industrial setting es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11219-012-9174-y
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/033472/EU/EvoTest - Evolutionary testing for complex systems/EVOTEST/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Vos ., TE.; Lindlar, FF.; Wilmes, B.; Windisch, A.; Baars, AI.; Kruse, PM.; Gross, H.... (2013). Evolutionary functional black-box testing in an industrial setting. Software Quality Journal. 21(2):259-288. doi:10.1007/s11219-012-9174-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://link.springer.com/article/10.1007%2Fs11219-012-9174-y es_ES
dc.description.upvformatpinicio 259 es_ES
dc.description.upvformatpfin 288 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 21 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 266132
dc.contributor.funder European Commission es_ES
dc.description.references Description of evolution engine parameters. http://guide.gforge.inria.fr/eeparams/EEngineParameters.pdf . Last accessed April 19, 2011. es_ES
dc.description.references ETF user manual and cookbook. http://evotest.iti.upv.es . Last accessed April 13, 2011. es_ES
dc.description.references GUIDE. http://gforge.inria.fr/projects/guide/ . Last accessed April 13, 2011. es_ES
dc.description.references Evotest. http://evotest.iti.upv.es (2006). Last accessed April 13, 2011. es_ES
dc.description.references Arcuri, A., White, D. R., Clark, J., & Yao, X. (2008). Multi-objective improvement of software using co-evolution and smart seeding. In: X. Li, M. Kirley, M. Zhang, D. G. Green, V. Ciesielski, H. A. Abbass, Z. Michalewicz, T. Hendtlass, K. Deb, K. C. Tan, J. Branke, & Y. Shi (Eds.), Proceedings of the 7th international conference on simulated evolution and learning (SEAL ’08), LNCS (Vol. 5361, pp. 61–70). Melbourne, Australia: Springer. es_ES
dc.description.references Baresel, A., Pohlheim, H., & Sadeghipour, S. (2003). Structural and functional sequence test of dynamic and state-based software with evolutionary algorithms. In GECCO (pp. 2428–2441). es_ES
dc.description.references Beizer B. (1990). Software testing techniques. London: International Thomson Computer Press. es_ES
dc.description.references Briand L. C. (2007). A critical analysis of empirical research in software testing. In: Empirical software engineering and measurement, 2007. First International Symposium on ESEM 2007 (pp. 1–8). es_ES
dc.description.references Bühler, O., & Wegener, J. (2004). Automatic testing of an autonomous parking system using evolutionary computation. In Proceedings of SAE 2004 world congress (pp. 115–122). es_ES
dc.description.references Bühler, O., & Wegener, J. (2008). Evolutionary functional testing. Computers & Operations Research, 35(10), 3144–3160. es_ES
dc.description.references Chan, B., Denzinger, J., Gates, D., Loose, K., & Buchanan, J. (2004). Evolutionary behaviour testing of commercial computer games. In Proceedings of CEC 2004, Portland (pp. 125–132). es_ES
dc.description.references DaCosta, L., Fialho, A., Schoenauer, M., & Sebag, M. (2008). Adaptive operator selection with dynamic multi-armed bandits. In Proceedings of the 10th annual conference on genetic and evolutionary computation, GECCO ’08 (pp. 913–920). New York, NY: ACM. DOI http://doi.acm.org/10.1145/1389095.1389272 . http://doi.acm.org/10.1145/1389095.1389272 . es_ES
dc.description.references Fewster, M., & Graham, D. (1999). Software test automation: effective use of test execution tools. New York, NY: ACM Press/Addison-Wesley Publishing Co. es_ES
dc.description.references Goldberg, D.~E. (1989). Genetic algorithms in search, optimization and machine learning. Boston: Addison Wesley. es_ES
dc.description.references Grochtmann, M., & Wegener, J. (1998). Evolutionary testing of temporal correctness. In: Proceedings of the 2nd international software quality week Europe (QWE 1998). Brussels, Belgium. es_ES
dc.description.references Gros, H. G. (2003). Evaluation of dynamic, optimisation-based worst-case execution time analysis. In: Proceedings of the international conference on information technology: Prospects and challenges in the 21st century, (Vol. 1, pp. 8–14). es_ES
dc.description.references Gross, H., Kruse, P. M., Wegener, J., Vos, T. (2009). Evolutionary white-box software test with the evotest framework: A progress report. In ICSTW ’09: Proceedings of the IEEE international conference on software testing, verification, and validation workshops (pp. 111–120). IEEE Computer Society, Washington, DC, USA. es_ES
dc.description.references Harman, M., Hu, L., Hierons, R., Baresel, A., & Sthamer, H. (2002). Improving evolutionary testing by flag removal. In Proceedings of the genetic and evolutionary computation conference (GECCO 2002) (pp. 1233 – 1240). Morgan Kaufmann, New York, USA. es_ES
dc.description.references Holland, J.H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press. es_ES
dc.description.references Jones, B., Sthamer, H., & Eyres, D. (1996). Automatic structural testing using genetic algorithms. The Software Engineering Journal, 11(5), 299–306. es_ES
dc.description.references Juristo, N., Moreno, A., & Vegas, S. (2004). Reviewing 25 years of testing technique experiments. Journal of Empirical Software Engineering 9(1), 7–44. es_ES
dc.description.references Keijzer, M., Merelo, J. J., Romero, G., & Schoenauer, M. (2001). Evolving objects: A general purpose evolutionary computation library. In Artificial evolution (pp. 231–244). http://citeseer.ist.psu.edu/keijzer01evolving.html . es_ES
dc.description.references Kitchenham, B. A., Pfleeger, S. L., Pickard, L. M., Jones, P. W., Hoaglin, D. C., Emam, K. E., et al. (2002). Preliminary guidelines for empirical research in software engineering. IEEE Transactions on Software Engineering, 28(8), 721–734. es_ES
dc.description.references Klimke, A. (2003) How to access Matlab from Java, IANS report 2003/005. Tech. rep., University of Stuttgart. http://preprints.ians.uni-stuttgart.de . es_ES
dc.description.references Kruse, P. M., Wegener, J., & Wappler, S. (2009). A highly configurable test system for evolutionary black-box testing of embedded systems. In GECCO ’09: Proceedings of the 11th annual conference on genetic and evolutionary computation (pp. 1545–1552). New York, NY: ACM. http://doi.acm.org/10.1145/1569901.1570108 . es_ES
dc.description.references Lethbridge, T. C., Sim, S. E., & Singer, J. (2005). Studying software engineers: Data collection techniques for software field studies. Empirical Software Engineering, 10(3), 311–341. es_ES
dc.description.references Lindlar, F., Windisch, A., & Wegener, J. (2010). Integrating model-based testing with evolutionary functional testing. In Proceedings of the 3rd international conference on software testing, verification, and validation workshops (ICSTW 2010) (pp. 163–172). Washington, DC: IEEE Computer Society. es_ES
dc.description.references McMinn, P. (2004). Search-based software test data generation: A survey. Software Testing, Verification and Reliability, 14(2), 105–156. es_ES
dc.description.references McMinn, P. (2011). Search-based software testing: Past, present and future. In Proceedings of the 4th international workshop on search-based software testing (SBST 2011). es_ES
dc.description.references Messina. http://www.berner-mattner.com/en/automotive-messina.php . Last accessed Feb 3, 2010. es_ES
dc.description.references Mueller, F., & Wegener, J. (1998). A comparison of static analysis and evolutionary testing for the verification of timing constraints. In RTAS ’98: Proceedings of the 4th IEEE real-time technology and applications symposium (p. 144). Washington, DC: IEEE Computer Society. es_ES
dc.description.references Pargas, R. P., Harrold, M. J., & Peck, R. R. (1999). Test-data generation using genetic algorithms. Journal of Software Testing, Verification and Reliability, 9(4), 263–282. es_ES
dc.description.references Perry, D. E., Porter, A. A., & Votta, L. G. (2000). Empirical studies of software engineering: A roadmap. In: ICSE ’00: Proceedings of the conference on the future of software engineering, (pp. 345–355). ACM. es_ES
dc.description.references Perry, D. E., Sim, S. E., & Easterbrook, S. (2005). Case studies for software engineers. In SEW ’05: Proceedings of the 29th annual IEEE/NASA software engineering workshop—Tutorial notes (pp. 96–159). Washington, DC: IEEE Computer Society. es_ES
dc.description.references Pohlheim, H. (2000). Evolutionäre algorithmen: Verfahren, operatoren und hinweise für die Praxis. Springer, Berlin: Heidelberg [u.a.]. es_ES
dc.description.references Sthamer, H., & Wegener, J. (2002). Using evolutionary testing to improve efficiency and quality in software testing. In Proceedings of 2nd Asia-Pacific conference on software testing. es_ES
dc.description.references Tlili, M., Sthamer, H., Wappler, S., & Wegener, J. (2006). Improving evolutionary real-time testing by seeding structural test data. In Proceedings of the congress on evolutionary computation (CEC) (pp. 3227–3233). IEEE. es_ES
dc.description.references Tlili, M., Wappler, S., Sthamer, H., & Wegener, J. (2006). Improving evolutionary real-time testing. In Proceedings of the 8th annual conference on genetic and evolutionary computation (GECCO) (pp. 1917–1924). New York: ACM Press. es_ES
dc.description.references Tracey, N., Clark, J., Mander, K., & McDermid, J. (2000). Automated test-data generation for exception conditions. Software: Practice and Experience, 30(1), 61–79. es_ES
dc.description.references Vos, T., Baars, A., Lindlar, F., Kruse, P., Windisch, A., & Wegener, J. (2010). Industrial scaled automated structural testing with the evolutionary testing tool. In Proceedings of the 3rd international conference on software testing, verification and validation (ICST2010), Paris (France) (pp. 175–184). IEEE Computer Society. es_ES
dc.description.references Wegener, J., Buhr, K., & Pohlheim, H. (2002). Automatic test data generation for structural testing of embedded software systems by evolutionary testing. In GECCO ’02: Proceedings of the genetic and evolutionary computation conference (pp. 1233–1240). San Francisco, CA: Morgan Kaufmann Publishers Inc. es_ES
dc.description.references Wegener, J., Grimm, K., Grochtmann, M., Sthamer, H., & Jones, B. (1996). Systematic testing of real-time systems. In Proceedings of the 4th European international conference on software testing, analysis and review. Amsterdam, The Netherlands. es_ES
dc.description.references Windisch, A., & Al Moubayed, N. (2009). Signal generation for search-based testing of continuous systems. In Proceedings of the 2nd international conference on software testing, verification, and validation workshops (pp. 121–130). Washington, DC: IEEE Computer Society. es_ES
dc.description.references Windisch, A., Lindlar, F., Topuz, S., & Wappler, S. (2009). Evolutionary functional testing of continuous control systems. In GECCO ’09: Proceedings of the 11th annual conference on genetic and evolutionary computation (pp. 1943–1944). New York, NY: ACM. es_ES
dc.description.references Windisch, A., Lindlar, F., Topuz, S., & Wappler, S. (2009). Evolutionary functional testing of continuous control systems. In Proceedings of the 11th annual conference on genetic and evolutionary computation (GECCO) (pp. 1943–1944). New York, NY: ACM. es_ES


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