Systematic Literature Review on Web Performance Testing
Resumo
Performance Testing is essential to ensure the quality and scalability of Web applications. A well-defined process may guide Performance Testing Engineer in conducting this task. We intended to enlighten some major inputs related to web performance testing. For this, we have formulated and executed a given protocol, according to the Systematic Literature Review (SLR) protocol in Software Engineering. So, 37 papers were selected/analyzed and we have extracted their most relevant contribution in order to answer our research questions. This analysis enabled us discovering preeminent performance testing profiles/roles, approaches, artifacts, methods, stages or phases and activity flows that have been reported in the literature. We believe that, despite those several studies that mapping performance test context, there are a few remarks in which a clarification might be needed, once there is no well-established process that comprises the whole activities mapped as well as established a relation with other studies. Therefore, this study intends to provide relevant input that one may establish a novel web performance testing process.
Palavras-chave:
Performance Testing, Systematic Literature Review
Referências
A. Kitchenham, B. (2007).Guidelines for performing Systematic Literature Reviews insoftware engineering. EBSE Technical Report EBSE-2007-01.
Bernardino, M., Zorzo, A. F., and Rodrigues, E. M. (2016). Canopus: A domain-specificlanguage for modeling performance testing. InIEEE International Conference onSoftware Testing, Verification and Validation (ICST), pages 157–167. IEEE
Caldiera, V. R. B.-G. and Rombach, H. D. (1994). Goal question metric paradigm.Ency-clopedia of software engineering, 1:528–532
Freitas, A. and Vieira, R. (2014). An ontology for guiding performance testing. In2014IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelli-gent Agent Technologies (IAT), volume 1, pages 400–407. IEEE.
Huang, X., Wang, W., Zhang, W., Wei, J., and Huang, T. (2011). An adaptive performancemodeling approach to performance profiling of multi-service web applications. InProc. International Computer Software and Applications Conference, pages 4–13.
Meier, J., Farre, C., Bansode, P., Barber, S., and Rea, D. (2007).Performance testingguidance for web applications: patterns & practices. Microsoft press.
Molyneaux, I. (2009).The art of application performance testing: Help for programmersand quality assurance. ” O’Reilly Media, Inc.”
Pfau, J., Smeddinck, J. D., and Malaka, R. (2017). Automated Game Testing withICARUS: Intelligent Completion of Adventure Riddles via Unsupervised Solving. InExtended Abstracts Publication of the Annual Symposium on Computer-Human Inter-action in Play, pages 153–164, New York, NY, USA. ACM
Rodrigues, E., Bernardino, M., Costa, L., Zorzo, A., and Oliveira, F. (2015). Pletsperf-amodel-based performance testing tool. In2015 IEEE 8th International Conference onSoftware Testing, Verification and Validation (ICST), pages 1–8. IEEE.
Russell, S. J. and Norvig, P. (2016).Artificial intelligence: a modern approach. Malaysia;Pearson Education Limited,
Souza, F. C., Santos, A., Andrade, S., Durelli, R., Durelli, V., and Oliveira, R. (2018).Au-tomating Search Strings for Secondary Studies, chapter 558, pages 839–848. SpringerInternational Publishing.
Subraya, B. M. (2006).Integrated approach to web performance testing: A practitioner’sguide.
Tselikis, C., Mitropoulos, S., and Douligeris, C. (2007). An evaluation of the middle-ware’s impact on the performance of object oriented distributed systems.Journal ofSystems and Software, 80(7):1169–1181.
Van Der Ster, D. C., Elmsheuser, J., Garcia, M. U., and Paladin, M. (2011). Hammer-Cloud: A stress testing system for distributed analysis. InJournal of Physics: Confer-ence Series, volume 331, Taipei, Taiwan.
Wohlin, C., Runeson, P., Hst, M., Ohlsson, M. C., Regnell, B., and Wessln, A. (2012).Ex-perimentation in Software Engineering. Springer Publishing Company, Incorporated.
Woodside, M., Franks, G., and Petriu, D. C. (2007). The future of software performanceengineering. InFuture of Software Engineering, pages 171–187. IEEE.
Xu, X., Jin, H., Wu, S., Tang, L., and Wang, Y. (2014). URMG: Enhanced CBMG-basedmethod for automatically testing web applications in the cloud.Tsinghua Science andTechnology, 19(1):65–75.
Yin, J., Ming, Z., Xiao, Z., and Wang, H. (2008). A web performance modeling processbased on the methodology of learning from data. InProceedings of the 9th International Conference for Young Computer Scientists, ICYCS 2008, pages 1285–1291,Zhang Jia Jie, Hunan, China.
Bernardino, M., Zorzo, A. F., and Rodrigues, E. M. (2016). Canopus: A domain-specificlanguage for modeling performance testing. InIEEE International Conference onSoftware Testing, Verification and Validation (ICST), pages 157–167. IEEE
Caldiera, V. R. B.-G. and Rombach, H. D. (1994). Goal question metric paradigm.Ency-clopedia of software engineering, 1:528–532
Freitas, A. and Vieira, R. (2014). An ontology for guiding performance testing. In2014IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelli-gent Agent Technologies (IAT), volume 1, pages 400–407. IEEE.
Huang, X., Wang, W., Zhang, W., Wei, J., and Huang, T. (2011). An adaptive performancemodeling approach to performance profiling of multi-service web applications. InProc. International Computer Software and Applications Conference, pages 4–13.
Meier, J., Farre, C., Bansode, P., Barber, S., and Rea, D. (2007).Performance testingguidance for web applications: patterns & practices. Microsoft press.
Molyneaux, I. (2009).The art of application performance testing: Help for programmersand quality assurance. ” O’Reilly Media, Inc.”
Pfau, J., Smeddinck, J. D., and Malaka, R. (2017). Automated Game Testing withICARUS: Intelligent Completion of Adventure Riddles via Unsupervised Solving. InExtended Abstracts Publication of the Annual Symposium on Computer-Human Inter-action in Play, pages 153–164, New York, NY, USA. ACM
Rodrigues, E., Bernardino, M., Costa, L., Zorzo, A., and Oliveira, F. (2015). Pletsperf-amodel-based performance testing tool. In2015 IEEE 8th International Conference onSoftware Testing, Verification and Validation (ICST), pages 1–8. IEEE.
Russell, S. J. and Norvig, P. (2016).Artificial intelligence: a modern approach. Malaysia;Pearson Education Limited,
Souza, F. C., Santos, A., Andrade, S., Durelli, R., Durelli, V., and Oliveira, R. (2018).Au-tomating Search Strings for Secondary Studies, chapter 558, pages 839–848. SpringerInternational Publishing.
Subraya, B. M. (2006).Integrated approach to web performance testing: A practitioner’sguide.
Tselikis, C., Mitropoulos, S., and Douligeris, C. (2007). An evaluation of the middle-ware’s impact on the performance of object oriented distributed systems.Journal ofSystems and Software, 80(7):1169–1181.
Van Der Ster, D. C., Elmsheuser, J., Garcia, M. U., and Paladin, M. (2011). Hammer-Cloud: A stress testing system for distributed analysis. InJournal of Physics: Confer-ence Series, volume 331, Taipei, Taiwan.
Wohlin, C., Runeson, P., Hst, M., Ohlsson, M. C., Regnell, B., and Wessln, A. (2012).Ex-perimentation in Software Engineering. Springer Publishing Company, Incorporated.
Woodside, M., Franks, G., and Petriu, D. C. (2007). The future of software performanceengineering. InFuture of Software Engineering, pages 171–187. IEEE.
Xu, X., Jin, H., Wu, S., Tang, L., and Wang, Y. (2014). URMG: Enhanced CBMG-basedmethod for automatically testing web applications in the cloud.Tsinghua Science andTechnology, 19(1):65–75.
Yin, J., Ming, Z., Xiao, Z., and Wang, H. (2008). A web performance modeling processbased on the methodology of learning from data. InProceedings of the 9th International Conference for Young Computer Scientists, ICYCS 2008, pages 1285–1291,Zhang Jia Jie, Hunan, China.
Publicado
11/11/2020
Como Citar
LEGRAMANTE, Guilherme; BERNARDINO, Maicon; RODRIGUES, Elder Macedo; BASSO, Fábio.
Systematic Literature Review on Web Performance Testing. In: ESCOLA REGIONAL DE ENGENHARIA DE SOFTWARE (ERES), 4. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 285-295.
DOI: https://doi.org/10.5753/eres.2020.13739.
