KAIRÓS: Intelligent System for Scenarios Recommendation at the Beginning of Software Process Improvement

Ana Marys Garcia Rodríguez, Yadian Guillermo Pérez Betancourt, Juan Pedro Febles Rodríguez, Yaimí Trujillo Casañola, Alejandro Perdomo Vergara

Abstract


Software Process Improvement provides benefits to organizations, however, efforts to improve aren't guided by the combined use of Critical Success Factors and Good Practices to be applied, dedicating resources without a prior analysis to guide the actions intentionally. The research proposes an intelligent system for decision making support in Software Process Improvement, taking as reference the Critical Success Factors and Good Practices that an organization can apply to improve its state. In order to achieve this, an intelligent system is conceived, which, based on Artificial Intelligence techniques, optimizes improvement scenarios through the implementation of a genetic algorithm and the application of rules of association between good practices and critical success factors, predicts the success of scenarios improvement through an evolutionary artificial neural network and offers recommendations to achieve them. The methods used to validate the results corroborated the contribution and usefulness of the proposal.


Full Text:

PDF

References


Dounos, P. and G. Bohoris (2010). Factors for the Design of CMMI-based Software Process Improvement Initiatives. Conference on Informatics (PCI), 2010 14th Panhellenic, IEEE Xplorer Digital Library, Tripoli, pp. 43 - 47.

Montoni, M. A. and A. R. Rocha (2010). Applying Grounded Theory to Understand Software Process Improvement Implementation. Proceedings of the 2010 Seventh International Conference on the Quality of Information and Communications Technology, IEEE Computer Society. pp. 25-34.

Niazi, M., D. Wilson and D. Zowghi (2006). Critical success factors for software process improvement implementation: an empirical study. Software Process: Improvement and Practice, Vol. 11, num. 2, pp. 193-211. ISSN 1099-1670.

Niazi, M., M. A. Babar and J. M. Verner (2010). Software Process Improvement barriers: A cross-cultural comparison. Information and Software Technology, Vol. 52, num. 11, pp. 1204-1216. ISSN 0950-5849.

Trujillo Casañola, Y (2014). Modelo para Valorar las Organizaciones Desarrolladoras de Software al Iniciar la Mejora de Procesos, Doctorado en Ciencias Técnicas, Tutors A. Febles Estrada and G. León Rodríguez, desarrollada en Diección de Calidad de Software, 2014, Informatics Sciences University, pp. 210.

Fernández Díaz, H., N. Milán Cristo, A. M. Garcia Rodríguez and Y. Trujillo Casañola (2016). Bases teóricas para un procedimiento que evalúe cuantitativamente la influencia de los Factores Críticos de Éxito en la Mejora de Procesos. Informática 2016. VII Taller Internacional de Calidad en las Tecnologías de la Información y las Comunicaciones. La Habana, XVI Convención y Feria Internacional INFORMÁTICA 2016.

Niazi, M., D. Wilson and D. Zowghi (2005). A maturity model for the implementation of software process improvement: an empirical study. J. Syst. Softw., Vol. 74, num. 2, pp. 155-172. ISSN 0164-1212.

Niazi, M., D. Wilson and D. Zowghi (2005). A framework for assisting the design of effective software process improvement implementation strategies. Journal of Systems and Software, Vol. 78, num. 2, pp. 204-222. ISSN 0164-1212.

Clarke, P. and R. O’Connor (2010). Harnessing ISO/IEC 12207 to Examine the Extent of SPI Activity in an Organization. European Conference on Software Process Improvement, Springer, pp. 25-36.

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, 1st ed., Addison-Wesley Longman Publishing Co., Inc. Boston, MA, pp. 412. ISBN 0201157675.

Pavez-Lazo, B., J. Soto-Cartes, C. Urrutia and M. Curilem (2009). Selección determinística y cruce anular en algoritmos genéticos: aplicación a la planificación de unidades térmicas de generación, Ingeniare. Revista chilena de ingeniería, Vol. 17, num. 2, pp. 175-181. ISSN 0718-3305.

Tallón, B. A. J. (2013). Nuevos modelos de Redes Neuronales Evolutivas para Clasificación. Aplicación a Unidades Producto y Unidades Sigmoide, Doctorado en Ingeniería y Tecnología del Software, Tutors M. C. Hervás and S. J. C. Riquelme, Sevilla.

Tallón Ballesteros, A. J., M. C. Hervás, J. C. Riquelme Santos and R. Ruiz (2013). Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems, Neurocomputing, Vol. 114, pp. 107-117. ISSN 0925-2312.

Tostado, S. S. E., R. M. Ornelas, J. A. Espinal and S. H. J. Puga (2016). Implementación de Algoritmos de Inteligencia Artificial para el Entrenamiento de Redes Neuronales de Segunda Generación, Jóvenes en la Ciencia, Vol. 2, num. 1, pp. 5. ISSN 2395-9797.

Martín, D., A. Rosete, J. Alcalá-Fdez and F. Herrera (2014). QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules, Information Sciences, Vol. 258, pp. 1-28. ISSN 0020-0255.

Oviedo Carrascal, E. A., A. I. Oviedo Carrascal and G. L. Vélez Saldarriaga (2015). MINERÍA DE DATOS: APORTES Y TENDENCIAS EN EL SERVICIO DE SALUD DE CIUDADES INTELIGENTES, Revista Politécnica, Vol. 11, num. 20. ISSN 1900-2351.

Garcia Rodríguez, A. M., Y. Trujillo Casañola, R. G. Rivero Morales, M. A. Verdera Marcano and O. Santos Acosta (2016). Red Neuronal Artificial para el pronóstico de éxito en la Mejora de Procesos de Software, Congreso INFO 2016, Instituto de Información Científica y Tecnología (IDICT), La Habana.

Garcia Rodríguez, A. M., Y. Trujillo Casañola and A. Perdomo Vergara (2016). Optimización de estados en la mejora de procesos de software. Enl@ce, Vol. 13, num. 2. ISSN 1690-7515.

Torres, M., K. Paz and F. Salazar (2006). Tamaño de una muestra para una investigación de mercado.

Gang, Y., Z. Hong, W. Lei and L. Ying (2009). An implementation of improved apriori algorithm, International Conference on Machine Learning and Cybernetics, pp. 1565-1569. ISBN 2160-133X.

Singh, J., H. Ram and D. J. Sodhi (2013). Improving efficiency of apriori algorithm using transaction reduction, International Journal of Scientific and Research Publications, Vol. 3, num. 1, pp. 1-4.

Yabing, J. (2013). Research of an improved apriori algorithm in data mining association rules, International Journal of Computer and Communication Engineering, Vol. 2, num. 1, pp. 25. ISSN 2010-3743.

Lin, X. (2014). Mr-apriori: Association rules algorithm based on mapreduce, Conference on Software Engineering and Service Science (ICSESS), 2014 5th IEEE International, IEEE. pp. 141-144. ISBN 1479932795.

Pradhan, T., S. R. Mishra and V. K. Jain (2014). An effective way to achieve excellence in research based learning using association rules, International Conference on Data Mining and Intelligent Computing (ICDMIC), pp. 1-4.




DOI: https://doi.org/10.31449/inf.v42i4.2066

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.