Subjectivity reducing in software version criticality classification with the support of an expert system

Authors

DOI:

https://doi.org/10.33448/rsd-v11i1.25132

Keywords:

Subjectivity Reduction; Criticality Classification; Expert System; Software Version Release; Key performance-indicators.

Abstract

In the software version release management process, there is a need, on the part of human specialists, to classify the criticality of each software version. However, the subjectivity of this classification may be present according to the experience acquired by specialists over the years. To reduce subjectivity in the process, an Artificial Intelligence technique called Expert System (ES) can be applied to represent the knowledge of human specialists and use it in problem solving. Thus, the aim of this paper was to reduce the subjectivity in the criticality classification of the software version with the support of the Expert System. To this end, a questionnaire was developed with the objective of obtaining the criticality opinions classified as High, Medium and Low in each specialist's software version to assist in the preparation of the ES production rules.  ES generated 17 production rules with a 100% confidence level applied to a production database. The results of the classification carried out by the ES corresponded to the classification carried out by the specialists in the production base, that is, the ES was able to represent their knowledge. Then, another questionnaire was applied to the specialists to verify the perception of satisfaction regarding the use of the ES with a result obtained of 4.8, considered satisfactory. It was concluded, then, that the ES supported the reduction of subjectivity in the classification of the criticality of software version.

References

Arrivabene, A., Sassi, R. J., Andrelo, P. F. A., Moura, M. L. A. De O (2021). Analysis of the impact of adequacy on operational information technology processes to the requirements of the Sarbanes-Oxley act in a financial company. Research, Society and Development, 10(1), e7710111374. 10.33448/rsd-v10i1.11374.

Axelos. (2013) Global best practice. ITIL Maturity Model and Self-Assessment Service: User Guide. Axelos Limited. http://www.axelos.com.

Babar, M. I., Ghazali, M., Jawawi, D. N. A., Shamsuddin, S. M., & Ibrahim, N. (2015). PHandler: An expert system for a scalable software requirements prioritization process. Knowledge-Based Systems, 84, 179-202. https://doi.org/10.1016/j.knosys.2015.04.010

Barros, M. D., & Salles, C. A. L. (2015). Mapping of the Scientific production on the Itil application published in the national and international literature, Procedia Computer Science, 55, 102-111. 10.1016/j.procs.2015.07.013

Castelli, M., Manzoni, L., Vanneschi, L., & Popovič, A. (2017). An Expert System for Extracting Knowledge From Customers’ Reviews: The Case of Amazon. com, inc. Expert Systems With Applications, 84, 117-126. https://doi.org/10.1016/j.eswa.2017.05.008.

Cruz-Hinojosa, N. J., & Gutiérrez-De-Mesa, J. A. (2016). Literature review of the situation research faces in the application of ITIL in Small and Medium Enterprises. Computer Standards & Interfaces, 48, 124-138. doi.org/10.1016/j.csi.2016.05.001

Napolitano, D. M. R.; & Sassi, R. J. (2018). Um Sistema de Inferência Fuzzy para Análise de Riscos em Projetos Baseado em Matrizes de Probabilidade e Impacto. NAVUS Revista de Gestão e Tecnologia, 8, 69-89.

Durkin, J. (1994) Expert systems: design and development. Macmillan Publishing.

Dymova, L., Sevastjanov, P., & Kaczmarek, K. (2016). A Forex Trading Expert System Based on a New Approach to The Rule-base Evidential Reasoning. Expert Systems With Applications, 51, 1–13. doi.org/10.1016/j.eswa.2015.12.028.

Farias, E. B. P., Gatto, D. D. O., Romero, M., Moura, M. L. A. O., & Sassi. R. (2021). Processo de Enfermagem Apoiad por sistema Especialista na Aplicação das Escalas de Glasgow e Braden em um Hospital Público Brasileiro. NAVUS Revista de Gestão e Tecnologia, 11, 114-129.

Ferreira, C., Nery, A., & Pinheiro, P. C. (2016). A Multi-Criteria Model in Information Technology Infrastructure Problems, Procedia Computer Science, 91, 642-651. 10.1016/j.procs.2016.07.161.

García-Valls, M.; Escribano-Barreno, J.; Munoz, J. G. (2018). An extensible collaborative framework for monitoring software quality in critical systems. Information and Software Technology, v. 107, p. 3-17. 10.1016/j.infsof.2018.10.005.

Geissman, J. R., & Shultz, R. D. (1988). Verification and validation of expert systems. AI Expert, San Francisco, 1(1), 26-33.

Heeager, L. T., & Nielsen, P. A. (2018). A Conceptual Model Of Agile Software Development In A Safety-Critical Context: A Systematic Literature Review. Information And Software Technology, 103, 22-39. 10.1016/J.Infsof.2018.06.004.

ITIL. (2013) ITIL Service Lifecycle Publication Suite. Editora Tso, Edição: Uk Ed.

Jia, G., Ming, Y., Bowen, Z., Yuxin, Z., Jun, Y., & Xinyu, D. (2018). Nuclear Safety-Critical Digital Instrumentation And Control System Software: Reliability Demonstration. Annals Of Nuclear Energy, 120, 516-527. 10.1016/J.Anucene.2018.06.003.

Khan, A. R.; Rehman, Z. U.; & Amim, H. U. (2011). Knowledge-Based System’s Modeling for Software Process Model Selection. International Journal of Advanced Computer Science and Applications (IJACSA), 2(2), 20-25. https://doi.org/10.14569/IJACSA.2011.020205

Lee, S. H., Lee, S. L, Park, J., Lee, E., & Kang, H. G. (2018). Development Of Simulation-Based Testing Environment For Safety-Critical Software. Nuclear Engineering And Technology, 50, 570-581. 10.1016/J.Net.2018.02.007.

LIA. (2017). ExSinta Versão 1.1 Uma Ferramenta Visual Para Criação De Sistemas Especialistas Manual Do Usuário. Laboratório De Inteligência Artificial. de: .

Liao, S. (2005). Expert System Methodologies And Applications – A Decade Review From 1995 To 2004. Expert Systems With Applications. 28, 93-103. Https://Doi.Org/10.1016/J.Eswa.2004.08.003.

Monedero, I., León, C, Denda, R., & Luque, J. (2008). Datacab: A Geographical-Information System Based Expert System For The Design Of Cable Networks. Expert Systems, 25(4), 335–348. Doi.Org/10.1111/J.1468-0394.2008.00445.X.

OGC. (2011). Office Of Government Commerce. ITIL - Service Strategy, Norwich: TSO Information & Publishing Solutions, 2011.

Pannu, A. (2015). Survey On Expert System And Its Research Areas. International Journal Of Engineering And Innovative Technology (IJEIT), 4 (10), 104-108, 2015.

Paschek, D., Rennung, F., Trusculescu, A., & Draghici, A. (2016). Corporate Development With Agile Business Process Modeling As A Key Success Factor. Procedia Computer Science, 100, 1168-1175. Https://Doi.Org/10.1016/J.Procs.2016.09.273.

Rezende, A. V.; Silva, A.; Britto, A.; & Amaral, R. (2019). Software project scheduling problem in the context of search-based software engineering: A systematic review. Journal of Systems and Software. 155, 43-56.

Roldán-García, M. D. M., García-Nieto, J., & Aldana-Montes, J. F. (2017). Enhancing Semantic Consistency In Anti-Fraud Rule-Based Expert Systems. Expert Systems With Applications, Málaga, Spain, 90, 332-343. Http://Dx.Doi.Org/10.1016/J.Eswa.2017.08.036.

Softplan. “Quem Somos” (2021). Disponível Em: http://www.softplan.com.br/a-softplan/quem-somos/. Acesso Em 21/12/2021.

Wagner, W. P. (2017). Trends In Expert System Development: A Longitudinal Content Analysis Of Over Thirty Years Of Expert System Case Studies. Expert Systems With Applications, 76, 85-96. 10.1016/J.Eswa.2017.01.028.

Yin, R. K. (2016). Pesquisa Qualitativa Do Início Ao Fim. (2a ed.).

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Published

09/01/2022

How to Cite

GATTO, D. D. de O. .; SASSI, R. J. . Subjectivity reducing in software version criticality classification with the support of an expert system. Research, Society and Development, [S. l.], v. 11, n. 1, p. e37811125132, 2022. DOI: 10.33448/rsd-v11i1.25132. Disponível em: https://www.rsdjournal.org/index.php/rsd/article/view/25132. Acesso em: 19 apr. 2024.

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Section

Exact and Earth Sciences