Machine learning aplicado al análisis del rendimiento de desarrollos de software

Victor Daniel Gil-Vera; Cristian Seguro-Gallego

doi:10.33571/rpolitec.v18n35a9

Autores

Victor Daniel Gil-Vera Ingeniero de Sistemas, Docencia e Investigación, Grupo de Investigación SISCO. Universidad Católica Luis Amigó https://orcid.org/0000-0003-3895-4822
Cristian Seguro-Gallego Ingeniero de Sistemas, Docencia e Investigación, Grupo de Investigación SISCO. Universidad Católica Luis Amigó

DOI:

https://doi.org/10.33571/rpolitec.v18n35a9

Palavras-chave:

Análise de Desempenho, Inteligência Artificial, Modelação Preditiva, Testes de Desempenho

Resumo

Os testes de desempenho são cruciais para medir a qualidade dos desenvolvimentos de software, pois permitem identificar aspectos que precisam de ser melhorados a fim de alcançar a satisfação do cliente. O objectivo deste trabalho era identificar a técnica óptima de Machine Learning para prever se um desenvolvimento de software satisfaz ou não os critérios de aceitação do cliente. Foi utilizada uma base de dados de informações obtidas a partir de testes de desempenho de serviços web e a métrica de qualidade F1-score. Conclui-se que, embora a técnica da Random Forest tenha obtido a melhor pontuação, não é correcto dizer que é a melhor técnica de Aprendizagem Automática; a quantidade e qualidade dos dados utilizados na formação desempenham um papel muito importante, bem como um processamento adequado da informação.

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Referências

ISO/IEC. (2011). BSI Standards Publication Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — System and software quality models. BSI Standards Publication. https://www.iso.org/standard/35733.html

Apte, V., Devidas, T. V. S. V., Akhilesh, G., & Anshul, K. (2017). AutoPerf : Automated Load Testing and Resource Usage Profiling of Multi-Tier Internet Applications. Proceedings of the 8th ACM/SPEC on Interna-tional Conference on Performance Engineering, 115–126. https://doi.org/10.1145/3030207.3030222

Bezemer, C., Eismann, S., Ferme, V., Grohmann, J., Heinrich, R., Jamshidi, P., Shang, W., Hoorn, A. Van, Villavicencio, M., Walter, J., & Willnecker, F. (2019). How is Performance Addressed in DevOps? A Survey on Industrial Practices. 45–50. https://doi.org/10.1145/3297663.3309672

Mohammad, R., & Hamad, H. (2019). Scalable Quality and Testing Lab (SQTL): Mission- Critical Applica-tions Testing. 2019 International Conference on Computer and Information Sciences (ICCIS), 1–7. https://doi.org/10.1109/ICCISci.2019.8716404

Arif, M.M., Shang, W. & Shihab, E. Empirical study on the discrepancy between performance testing re-sults from virtual and physical environments. Empir Software Eng 23, 1490–1518 (2018). https://doi.org/10.1007/s10664-017-9553-x

Syer, M.D., Shang, W., Jiang, Z.M. et al. Continuous validation of performance test workloads. Autom Softw Eng 24, 189–231 (2017). https://doi.org/10.1007/s10515-016-0196-8

Lenka, R. K., & Dey, M. R. (2018). Performance and Load Testing: Tools and Challenges. 2257–2261. https://doi.org/10.1109/ICRIEECE44171.2018.9009338

Oriol, M. (2020). Systematic Literature study on estimation and prioritization of quality requirements in software development. June, 24–27. https://doi.org/10.23919/CISTI49556.2020.9140957

Pradeep, S., & Sharma, Y. K. (2019). A Pragmatic Evaluation of Stress and Performance Testing Tech-nologies for Web Based Applications. 2019 Amity International Conference on Artificial Intelligence (AICAI), 399–403. https://doi.org/10.1109/AICAI.2019.8701327

Silva, Lady. (2020). Model Driven Engineering for Performance Testing in Mobile Applications. https://doi.org/10.1109/SEEDA-CECNSM49515.2020.9221828

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpreta-tions, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004

Jacob, A. (2018). Scrutiny on Various Approaches of Software Performance Testing Tools. Iceca, 509–515. https://doi.org/10.1109/ICECA.2018.8474876

Moghadam, M. H., Saadatmand, M., Borg, M., & Bohlin, M. (2020). Poster: Performance Testing Driven by Reinforcement Learning. 402–405. https://doi.org/10.1109/ICST46399.2020.00048

Li, H., Li, X., Wang, H., Zhang, J., & Jiang, Z. (2019). Research on Cloud Performance Testing Model. 2019 IEEE 19th International Symposium on High Assurance Systems Engineering (HASE), 2015, 179–183. https://doi.org/10.1109/HASE.2019.00035

Ahmad, T., Ashraf, A., Truscan, D., Domi, A., & Porres, I. (2020). Using Deep Reinforcement Learning for Exploratory Performance Testing of Software Systems with Multi-Dimensional Input Spaces. 8. https://doi.org/10.1109/ACCESS.2020.3033888

Postolski, I., Braberman, V., Garbervetsky, D., & Uchitel, S. (2019). Simulator-Based Diff-Time Perfor-mance Testing. 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), 81–84. https://doi.org/10.1109/ICSE-NIER.2019.00029

Lopez, L., Mart, S., Vollmer, A. M., Rodr, P., Franch, X., Oivo, M., Mart, S., Vollmer, A. M., Franch, X., Karhapää, P., Lopez, L., Burgués, X., Vollmer, A. M., Rodríguez, P., & Franch, X. (2019). Management of quality requirements in agile and rapid software development: a systematic mapping study Woubshet Behutiye Martínez-Fernández. Information and Software Technology, 106225. https://doi.org/10.1016/j.infsof.2019.106225

Cares, C. (2014). Top Ten NFR to Survive in the “Brave New World” of E-government Applications. 2014 9th Iberian Conference on Information Systems and Technologies (CISTI), 6. https://doi.org/10.1109/CISTI.2014.6876999

Silva, A., & Barroso, J. (2016). A survey about the situation of the elicitation of non-functional require-ments. 11th Iberian Conference on Information Systems and Technologies (CISTI). https://doi.org/10.1109/CISTI.2016.7521427

Silva, A., Fortaleza, U. De, Albuquerque, A. B., Fortaleza, U. De, & Barroso, J. (2016). A Process for Creating the Elicitation Guide of Non-functional Requirements A Process for Creating the Elicitation Guide of Non- Functional Requirements. January. https://doi.org/10.1007/978-3-319-33622-0

Yu, L., Alégroth, E., Chatzipetrou, P., & Gorschek, T. (2020). Utilising CI environment for efficient and effective testing of NFRs ☆. 117(May 2019). https://doi.org/10.1016/j.infsof.2019.106199

Hasnain, M. (2019). An Efficient Performance Testing of Web Services. 2019 22nd International Multi-topic Conference (INMIC), 1–8. https://doi.org/10.1109/INMIC48123.2019.9022763

He, S., Manns, G., Saunders, J., Wang, W., Pollock, L., & Soffa, M. Lou. (2019). A Statistics-Based Performance Testing Methodology for Cloud Applications: Proceedings of the 2019 27th ACM Joint Meet-ing on European Software Engineering Conference and Symposium on the Foundations of Software Engi-neering, 188–199. https://doi.org/10.6084/m9.figshare.7749356

Shariff, S. M., Li, H., & Bezemer, C. (2019). Improving the Testing Efficiency of Selenium-based Load Tests. 14th International Workshop on Automation of Software Test, 14–20. https://doi.org/10.1109/AST.2019.00008

Alshazly, A. A., Elnainay, M. Y., El-zoghabi, A. A., & Abougabal, M. S. (2020). A cloud software life cycle process (CSLCP) model. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2020.11.004

Bhowmik, T., & Do, A. Q. (2018). Refinement and Resolution of Just-in-Time Requirements in Open Source Software and a Closer Look into Non-Functional Requirements. Journal of Industrial Information In-tegration. https://doi.org/10.1016/j.jii.2018.03.001

Flores-Rios, B. L., & Pino, F. J. (2018). Non-functional requirements elicitation based on stakeholders’ s knowledge management. 26, 142–156. https://doi.org/10.22395/rium.v17n32a8.

Jiang, Z. M., & Hassan, A. E. (2015). A Survey on Load Testing of Large-Scale Software Systems. 5589(2), 1–32. https://doi.org/10.1109/TSE.2015.2445340

Ayala-rivera, V., Murphy, J., Portillo-dominguez, A. O., Darisa, A., & Kaczmarski, M. (2018). One Size Does Not Fit All: In-Test Workload Adaptation for Performance Testing of Enterprise Applications. ACM/SPEC International Conference on Performance Engineering, December 2010, 211–222. https://doi.org/10.1145/3184407.3184418

Moghadam, M. H., Saadatmand, M., Borg, M., Bohlin, M., & Lisper, B. (2019). Machine Learning to Guide Performance Testing: An Autonomous Test Framework. 2019 IEEE International Conference on Soft-ware Testing, Verification and Validation Workshops (ICSTW), 164–167. https://doi.org/10.1109/ICSTW.2019.00046

Aichernig, B. K., Bauerst¨atter, P., J¨obstl, E., Kann, S., Journal, S. Q., Koroˇsec, R., Krenn, W., Mate-is, C., Schlick, R., & Schumi, R. (2019). Learning and statistical model checking of system response times. Software Quality Journal. https://doi.org/10.1007/s11219-018-9432-8

Leitner, P., & Bezemer, C. (2017). An Exploratory Study of the State of Practice of Performance Test-ing in Java-Based Open Source Projects. Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering - ICPE, 373–384. https://doi.org/10.1145/3030207.3030213

AlGhamdi, H. M., Bezemer, C., Shang, W., Hassan, A. E., & Flora, P. (2020). Towards reducing the time needed for load testing. Journal of Software: Evolution and Process, April, 1–17. https://doi.org/10.1002/smr.2276