Software project planning through comparison of Bio-inspired algorithms

Currently many organizations have adopted the development of software projects with agile methodologies, particularly Scrum, which has more than 20 years of development. In these methodologies, software is developed iteratively and delivered to the client in increments called releases. In the releas...

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Autores:
Silva, Jesús
Varela Izquierdo, Noel
NEIRA MOLINA, HAROLD ROBERTO
Pineda, Omar
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8036
Acceso en línea:
https://hdl.handle.net/11323/8036
https://doi.org/10.1007/978-981-15-6648-6_27
https://repositorio.cuc.edu.co/
Palabra clave:
Genetic algorithm
Agile software projects
Multi-target
Rights
openAccess
License
CC0 1.0 Universal
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/8036
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Software project planning through comparison of Bio-inspired algorithms
title Software project planning through comparison of Bio-inspired algorithms
spellingShingle Software project planning through comparison of Bio-inspired algorithms
Genetic algorithm
Agile software projects
Multi-target
title_short Software project planning through comparison of Bio-inspired algorithms
title_full Software project planning through comparison of Bio-inspired algorithms
title_fullStr Software project planning through comparison of Bio-inspired algorithms
title_full_unstemmed Software project planning through comparison of Bio-inspired algorithms
title_sort Software project planning through comparison of Bio-inspired algorithms
dc.creator.fl_str_mv Silva, Jesús
Varela Izquierdo, Noel
NEIRA MOLINA, HAROLD ROBERTO
Pineda, Omar
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Varela Izquierdo, Noel
NEIRA MOLINA, HAROLD ROBERTO
Pineda, Omar
dc.subject.spa.fl_str_mv Genetic algorithm
Agile software projects
Multi-target
topic Genetic algorithm
Agile software projects
Multi-target
description Currently many organizations have adopted the development of software projects with agile methodologies, particularly Scrum, which has more than 20 years of development. In these methodologies, software is developed iteratively and delivered to the client in increments called releases. In the releases, the goal is to develop system functionality that quickly adds value to the client’s business. At the beginning of the project, one or more releases are planned. For solving the problem of replanning in the context of releases, a model is proposed considering the characteristics of agile development using Scrum. The results obtained show that the algorithm takes a little less than 7 min for solutions that propose replanning composed by 16 sprints, which is equivalent to 240 days of project. They show that applying a repair operator increases the hypervolume quality
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-07-19
dc.date.accessioned.none.fl_str_mv 2021-03-17T16:17:46Z
dc.date.available.none.fl_str_mv 2021-03-17T16:17:46Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-6648-6_27
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8036
https://doi.org/10.1007/978-981-15-6648-6_27
https://repositorio.cuc.edu.co/
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language eng
dc.relation.references.spa.fl_str_mv 1. Semenkina, O.E., Popov, E.A., Ryzhikov, I.S.: Hierarchical scheduling problem in the field of manufacturing operational planning. In: IOP Conference Series: Materials Science and Engineering, vol. 537, no. 3, p. 032001. IOP Publishing (2019)
2. Phanden, R.K., Jain, A., Davim, J.P. (eds.): Integration of Process Planning and Scheduling: Approaches and Algorithms. CRC Press, Boca Raton (2019)
3. Jahr, M.: A hybrid approach to quantitative software project scheduling within agile frameworks. Project Manage. J. 45(3), 35–45 (2014)
4. Roque, L., Araújo, A.A., Dantas, A., Saraiva, R., Souza, J.: Human resource allocation in agile software projects based on task similarities. In: Sarro, F., Deb, K. (eds.) SSBSE 2016. LNCS, vol. 9962, pp. 291–297. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47106-8_25
5. Varas, J.M., et al.: MAXCMAS project: autonomous COLREGs compliant ship navigation. In: Proceedings of the 16th Conference on Computer Applications and Information Technology in the Maritime Industries (COMPIT) 2017, pp. 454–464 (2017)
6. Ge, Y.: Software project rescheduling with genetic algorithms. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence, vol. 1, pp. 439–443. IEEE, Shanghai (2009)
7. Ge, Y., Xu, B.: Dynamic staffing and rescheduling in software project management: a hybrid approach. PLoS ONE 11(6), e0157104 (2016)
8. Shen, X., Minku, L.L., Bahsoon, R., Yao, X.: Dynamic software project scheduling through a proactive-rescheduling method. Trans. Softw. Eng. 42(7), 658–686 (2016)
9. Shen, X.N., Minku, L.L., Marturi, N., Guo, Y.N., Han, Y.: A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling. Inf. Sci. 428, 1–29 (2018)
10. Song, Y.J., Zhang, Z.S., Song, B.Y., Chen, Y.W.: Improved genetic algorithm with local search for satellite range scheduling system and its application in environmental monitoring. Sustain. Comput. Inf. Syst. 21, 19–27 (2019)
11. Moosavi, S.H.S., Bardsiri, V.K.: Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng. Appl. Artif. Intell. 60, 1–15 (2017)
12. Zheng, Z., Guo, J., Gill, E.: Swarm satellite mission scheduling & planning using hybrid dynamic mutation genetic algorithm. Acta Astronaut. 137, 243–253 (2017)
13. Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)
14. Deng, M., et al.: A two-phase coordinated planning approach for heterogeneous earth-observation resources to monitor area targets. IEEE Trans. Syst. Man Cybern. Syst. (2020)
15. Ghoddousi, P., Ansari, R., Makui, A.: An improved robust buffer allocation method for the project scheduling problem. Eng. Optim. 49(4), 718–731 (2017)
16. Tomori, H., Hiyoshi, K.: Control of pneumatic artificial muscles using local cyclic inputs and genetic algorithm. Actuators 7(3), 36 (2018)
17. Ibraigheeth, M., Fadzli, S.A.: Core factors for software projects success. JOIV Int. J. Inf. Visual. 3(1), 69–74 (2019)
18. da Silva Arantes, J., da Silva Arantes, M., Toledo, C.F.M., Júnior, O.T., Williams, B.C.: An embedded system architecture based on genetic algorithms for mission and safety planning with UAV. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1049–1056 (2017)
19. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. 36(2), 1627–1637 (2019)
20. Viloria, A., Robayo, P.V.: Virtual network level of application composed IP networks connected with systems-(NETS Peer-to-Peer). Indian J. Sci. Technol. 9, 46 (2016)
21. Plice, L., Lau, B., Pisanich, G., Young, L.A.: Biologically inspired behavioral strategies for autonomous aerial explorers on Mars. In: 2003 IEEE Aerospace Conference Proceedings (Cat. No. 03TH8652), vol. 1, pp. 1–304. IEEE (2003)
22. Barbagallo, D., Di Nitto, E., Dubois, D.J., Mirandola, R.: A bio-inspired algorithm for energy optimization in a self-organizing data center. In: Weyns, D., Malek, S., de Lemos, R., Andersson, J. (eds.) SOAR 2009. LNCS, vol. 6090, pp. 127–151. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14412-7_7
23. Srivastava, P.R., Varshney, A., Nama, P., Yang, X.S.: Software test effort estimation: a model based on cuckoo search. Int. J. Bio Inspired Comput. 4(5), 278–285 (2012)
24. Sheta, A.F., Ayesh, A., Rine, D.: Evaluating software cost estimation models using particle swarm optimisation and fuzzy logic for NASA projects: a comparative study. Int. J. Bio Inspired Comput. 2(6), 365–373 (2010)
25. Tempesti, G.: Architectures and design methodologies for bio-inspired computing machines. In: SNF Professorship Application Research Plan (2003)
26. Chiang, H.S., Sangaiah, A.K., Chen, M.Y., Liu, J.Y.: A novel artificial bee colony optimization algorithm with SVM for bio-inspired software-defined networking. Int. J. Parallel Prog. 1–19 (2018)
27. Camacho, D., et al.: From ephemeral computing to deep bioinspired algorithms: new trends and applications. Future Gener. Comput. Syst. 88, 735–746 (2018)
28. Chis, M.: Introduction: a survey of the evolutionary computation techniques for software engineering. In: Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques, pp. 1–12. IGI Global (2010)
29. Wang, L., Shen, J.: Towards bio-inspired cost minimisation for data-intensive service provision. In: 2012 IEEE First International Conference on Services Economics, pp. 16–23. IEEE (2012)
30. Wang, J., Cao, J., Li, B., Lee, S., Sherratt, R.S.: Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Trans. Consum. Electron. 61(4), 438–444 (2015)
31. Chis, M., (ed.) Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques: Applications and Techniques. IGI Global (2010)
32. Sharma, T.K.: Estimating software reliability growth model parameters using opposition-based shuffled frog-leaping algorithm. In: Ray, K., Pant, M., Bandyopadhyay, A. (eds.) Soft Computing Applications, pp. 149–164. Springer, Singapore (2018)
33. Barocio, E., Regalado, J., Cuevas, E., Uribe, F., Zúñiga, P., Torres, P.J.R.: Modified bio-inspired optimisation algorithm with a centroid decision making approach for solving a multi-objective optimal power flow problem. IET Gener. Transm. Distrib. 11(4), 1012–1022 (2017)
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spelling Silva, JesúsVarela Izquierdo, NoelNEIRA MOLINA, HAROLD ROBERTOPineda, Omar2021-03-17T16:17:46Z2021-03-17T16:17:46Z2020-07-1918650929https://hdl.handle.net/11323/8036https://doi.org/10.1007/978-981-15-6648-6_27Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Currently many organizations have adopted the development of software projects with agile methodologies, particularly Scrum, which has more than 20 years of development. In these methodologies, software is developed iteratively and delivered to the client in increments called releases. In the releases, the goal is to develop system functionality that quickly adds value to the client’s business. At the beginning of the project, one or more releases are planned. For solving the problem of replanning in the context of releases, a model is proposed considering the characteristics of agile development using Scrum. The results obtained show that the algorithm takes a little less than 7 min for solutions that propose replanning composed by 16 sprints, which is equivalent to 240 days of project. They show that applying a repair operator increases the hypervolume qualitySilva, JesúsVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600NEIRA MOLINA, HAROLD ROBERTO-will be generated-orcid-0000-0003-3595-8086-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaRetractedCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Communications in Computer and Information Sciencehttps://link.springer.com/chapter/10.1007/978-981-15-6648-6_27Genetic algorithmAgile software projectsMulti-targetSoftware project planning through comparison of Bio-inspired algorithmsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Semenkina, O.E., Popov, E.A., Ryzhikov, I.S.: Hierarchical scheduling problem in the field of manufacturing operational planning. In: IOP Conference Series: Materials Science and Engineering, vol. 537, no. 3, p. 032001. IOP Publishing (2019)2. Phanden, R.K., Jain, A., Davim, J.P. (eds.): Integration of Process Planning and Scheduling: Approaches and Algorithms. CRC Press, Boca Raton (2019)3. Jahr, M.: A hybrid approach to quantitative software project scheduling within agile frameworks. Project Manage. J. 45(3), 35–45 (2014)4. Roque, L., Araújo, A.A., Dantas, A., Saraiva, R., Souza, J.: Human resource allocation in agile software projects based on task similarities. In: Sarro, F., Deb, K. (eds.) SSBSE 2016. LNCS, vol. 9962, pp. 291–297. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47106-8_255. Varas, J.M., et al.: MAXCMAS project: autonomous COLREGs compliant ship navigation. In: Proceedings of the 16th Conference on Computer Applications and Information Technology in the Maritime Industries (COMPIT) 2017, pp. 454–464 (2017)6. Ge, Y.: Software project rescheduling with genetic algorithms. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence, vol. 1, pp. 439–443. IEEE, Shanghai (2009)7. Ge, Y., Xu, B.: Dynamic staffing and rescheduling in software project management: a hybrid approach. PLoS ONE 11(6), e0157104 (2016)8. Shen, X., Minku, L.L., Bahsoon, R., Yao, X.: Dynamic software project scheduling through a proactive-rescheduling method. Trans. Softw. Eng. 42(7), 658–686 (2016)9. Shen, X.N., Minku, L.L., Marturi, N., Guo, Y.N., Han, Y.: A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling. Inf. Sci. 428, 1–29 (2018)10. Song, Y.J., Zhang, Z.S., Song, B.Y., Chen, Y.W.: Improved genetic algorithm with local search for satellite range scheduling system and its application in environmental monitoring. Sustain. Comput. Inf. Syst. 21, 19–27 (2019)11. Moosavi, S.H.S., Bardsiri, V.K.: Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng. Appl. Artif. Intell. 60, 1–15 (2017)12. Zheng, Z., Guo, J., Gill, E.: Swarm satellite mission scheduling & planning using hybrid dynamic mutation genetic algorithm. Acta Astronaut. 137, 243–253 (2017)13. Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)14. Deng, M., et al.: A two-phase coordinated planning approach for heterogeneous earth-observation resources to monitor area targets. IEEE Trans. Syst. Man Cybern. Syst. (2020)15. Ghoddousi, P., Ansari, R., Makui, A.: An improved robust buffer allocation method for the project scheduling problem. Eng. Optim. 49(4), 718–731 (2017)16. Tomori, H., Hiyoshi, K.: Control of pneumatic artificial muscles using local cyclic inputs and genetic algorithm. Actuators 7(3), 36 (2018)17. Ibraigheeth, M., Fadzli, S.A.: Core factors for software projects success. JOIV Int. J. Inf. Visual. 3(1), 69–74 (2019)18. da Silva Arantes, J., da Silva Arantes, M., Toledo, C.F.M., Júnior, O.T., Williams, B.C.: An embedded system architecture based on genetic algorithms for mission and safety planning with UAV. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1049–1056 (2017)19. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. 36(2), 1627–1637 (2019)20. Viloria, A., Robayo, P.V.: Virtual network level of application composed IP networks connected with systems-(NETS Peer-to-Peer). Indian J. Sci. Technol. 9, 46 (2016)21. Plice, L., Lau, B., Pisanich, G., Young, L.A.: Biologically inspired behavioral strategies for autonomous aerial explorers on Mars. In: 2003 IEEE Aerospace Conference Proceedings (Cat. No. 03TH8652), vol. 1, pp. 1–304. IEEE (2003)22. Barbagallo, D., Di Nitto, E., Dubois, D.J., Mirandola, R.: A bio-inspired algorithm for energy optimization in a self-organizing data center. In: Weyns, D., Malek, S., de Lemos, R., Andersson, J. (eds.) SOAR 2009. LNCS, vol. 6090, pp. 127–151. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14412-7_723. Srivastava, P.R., Varshney, A., Nama, P., Yang, X.S.: Software test effort estimation: a model based on cuckoo search. Int. J. Bio Inspired Comput. 4(5), 278–285 (2012)24. Sheta, A.F., Ayesh, A., Rine, D.: Evaluating software cost estimation models using particle swarm optimisation and fuzzy logic for NASA projects: a comparative study. Int. J. Bio Inspired Comput. 2(6), 365–373 (2010)25. Tempesti, G.: Architectures and design methodologies for bio-inspired computing machines. In: SNF Professorship Application Research Plan (2003)26. Chiang, H.S., Sangaiah, A.K., Chen, M.Y., Liu, J.Y.: A novel artificial bee colony optimization algorithm with SVM for bio-inspired software-defined networking. Int. J. Parallel Prog. 1–19 (2018)27. Camacho, D., et al.: From ephemeral computing to deep bioinspired algorithms: new trends and applications. Future Gener. Comput. Syst. 88, 735–746 (2018)28. Chis, M.: Introduction: a survey of the evolutionary computation techniques for software engineering. In: Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques, pp. 1–12. IGI Global (2010)29. Wang, L., Shen, J.: Towards bio-inspired cost minimisation for data-intensive service provision. In: 2012 IEEE First International Conference on Services Economics, pp. 16–23. IEEE (2012)30. Wang, J., Cao, J., Li, B., Lee, S., Sherratt, R.S.: Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Trans. Consum. Electron. 61(4), 438–444 (2015)31. Chis, M., (ed.) Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques: Applications and Techniques. IGI Global (2010)32. Sharma, T.K.: Estimating software reliability growth model parameters using opposition-based shuffled frog-leaping algorithm. In: Ray, K., Pant, M., Bandyopadhyay, A. (eds.) Soft Computing Applications, pp. 149–164. Springer, Singapore (2018)33. Barocio, E., Regalado, J., Cuevas, E., Uribe, F., Zúñiga, P., Torres, P.J.R.: Modified bio-inspired optimisation algorithm with a centroid decision making approach for solving a multi-objective optimal power flow problem. IET Gener. Transm. 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