Abstract
In software engineering, the development of software products raises a new set of development requirements each time. Considering the interaction between requirements, how to select an optimal subset of requirements becomes an important problem. In this paper, a fast method of requirements optimization is proposed, which can select an optimal subset from the next release of product development requirements under the limitation of user satisfactions and cost. The multiple requirements in this paper are limited by user satisfaction and cost. We mainly make the following contributions: (1) We define this problem as multi-objective problem for optimization. (2) Then particle swarm optimization (PSO) algorithm is used to adjust the convergence parameters of multiple object to search the optimal solution quickly. (3) Finally, the results of the algorithm are evaluated by using NDS number and time of multi-objective problem through fuzzy simulation data. Experimental results show that the algorithm is efficient and reliable, and can help developers make reasonable decisions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boehm, B.W.: Software engineering. IEEE Trans. Comput. C25(12), 1226–1241 (1977)
Shaukat, Z.S., Naseem, R., Zubair, M.: A dataset for software requirements risk prediction. In: 2018 IEEE International Conference on Computational Science and Engineering (CSE). IEEE (2018)
Bagnall, A.J., Rayward-Smith, V.J., Whittley, I.M.: The next release problem. Inf. Softw. Technol. 43(14), 883–890 (2001)
Durillo, J.J., Zhang, Y., Alba, E., et al.: A study of the bi-objective next release problem. Empir. Softw. Eng. 16(1), 29–60 (2011)
Praditwong, K., Harman, M., Yao, X., et al.: Software module clustering as a multi-objective search problem. IEEE Trans. Softw. Eng. 37(2), 264–282 (2011)
Baker, P., Harman, M., Steinhofel, K., et al.: Search based approaches to component selection and prioritization for the next release problem. In: International Conference on Software Maintenance, pp. 176–185 (2006)
Sun, Z., et al.: Designing and optimization of fuzzy sliding mode controller for nonlinear systems. Comput. Mater. Continua 61(1), 119–128 (2019)
Wenkai, C.: Status and development trend in software engineering. Inf. Rec. Mater. 6, 6–8 (2018)
Fan, X., Zhou, T.: Status and future of software engineering industry development strategy. Comput. Program. Skills Maint. 406 (04), 57–59 (2019)
Yanping, L.: Investigating the technical requirements for software development. Mod. Vocat. Educ. 36, 210–211 (2017)
Chen, J.: Research versioning software searches for the next technology and implementation. Nanjing University of Posts and Telecommunications (2018)
Rosenberg, R.S.: Simulation of genetic populations with biochemical properties. Ph.D. Thesis. University of Michigan, Michigan (1967)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Michigan (1975)
Goldberg, D.E.: Genetic Algorithm for Search, Optimization, and Machine Learning. Addison-Wesley Longman Pub lishing Co., Inc., Boston (1989)
Fonaeca, C.M., Fleming, P.J.: Genetic algorithm for multiobjective optimization: formulation, discussion and generation. In: Forrest, S., (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423. Morgan Kauffman Publishers, San Mateo (1993)
Zuo, L., et al.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3, 2687–2699 (2015)
Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics. ISORMS, vol. 272, pp. 311–351. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91086-4_10
Dasgupta, D. (ed.): Artificial Immune Systems and Their Applications. Springer, Heidelberg (2012)
Liu, Z., Xiang, B., Yuqing Song, H.L., Liu, Q.: An improved unsupervised image segmentation method based on multi-objective particle, swarm optimization clustering algorithm. Comput. Mater. Continua 58(2), 451–461 (2019)
Liu, W., Tang, Y., Yang, F., Dou, Y., Wang, J.: A multi-objective decision-making approach for the optimal location of electric vehicle charging facilities. Comput. Mater. Continua 60(2), 813–834 (2019)
Kennedy, J.: Swarm intelligence. In: Swarm intelligence. Morgan Kaufmann Publishers Inc. (2001)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks. IEEE (1995)
Alrezaamiri, H., Ebrahimnejad, A., Motameni, H.: Software requirement optimization using a fuzzy artificial chemical reaction optimization algorithm. Soft Comput. 23(20), 9979–9994 (2018). https://doi.org/10.1007/s00500-018-3553-7
Ebrahimnejad, A., Tavana, M., Alrezaamiri, H.: A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights. Measurement 93, 48–56 (2016)
Tajdin, A., Mahdavi, I., Mahdavi-Amiri, N., Sadeghpour-Gildeh, B.: Computing a fuzzy shortest path in a network with mixed fuzzy lengths using a-cut. Comput. Math Appl. 60(2), 989–1002 (2010)
Hassanzadeh, R., Mahdavi, I., Mahdavi-Amiri, N., Tajdin, A.: A genetic algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths. Math. Comp. Model. 57(1–2), 84–99 (2013)
Mahdavi, I., Tajdin, A., Hassanzadeh, R., et al.: Genetic algorithm for solving fuzzy shortest path problem in a network with mixed fuzzy arc lengths. In: AIP Conference Proceedings, vol. 1337, p. 265 (2011)
Alrezaamiri, H., Ebrahimnejad, A., Motameni, H.: Software requirement optimization using a fuzzy artificial chemical reaction optimization algorithm. Soft Comput. - Fusion Found. Methodol. Appl. 23, 9979–9994 (2019)
Acknowledgement
This work has been supported by the National Science Foundation of China Grant No. 61762092, “Dynamic multi-objective requirement optimization based on transfer learning”, and the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province, Grant No. 2017SE204, “Research on extracting software feature models using transfer learning”, and the National Science Foundation of China Grant No. 61762089, “The key research of high order tensor decomposition in distributed environment”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y. et al. (2020). Fuzzy Multi-objective Requirements for NRP Based on Particle Swarm Optimization. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-57881-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57880-0
Online ISBN: 978-3-030-57881-7
eBook Packages: Computer ScienceComputer Science (R0)