Estimation techniques for scrum: a qualitative systematic study

Every competitive IT industry cannot avoid underestimating their projects’ effort, cost, and time. Some scrum project is completed delayed and undergoes difficulties due to over budgeting and a lack of needed functions. Software project failures are caused by incorrect and imprecise estimation; thus...

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Autores:
Díaz-Martínez, Jorge L
Sanjay, Misra
Butt, Shariq Aziz
Ayeni, Foluso
Tipo de recurso:
Part of book
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9347
Acceso en línea:
https://hdl.handle.net/11323/9347
https://doi.org/10.1007/978-3-030-96299-9_77
https://repositorio.cuc.edu.co/
Palabra clave:
Agile methodology
Software development
Cost estimation techniques
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closedAccess
License
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
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dc.title.eng.fl_str_mv Estimation techniques for scrum: a qualitative systematic study
title Estimation techniques for scrum: a qualitative systematic study
spellingShingle Estimation techniques for scrum: a qualitative systematic study
Agile methodology
Software development
Cost estimation techniques
title_short Estimation techniques for scrum: a qualitative systematic study
title_full Estimation techniques for scrum: a qualitative systematic study
title_fullStr Estimation techniques for scrum: a qualitative systematic study
title_full_unstemmed Estimation techniques for scrum: a qualitative systematic study
title_sort Estimation techniques for scrum: a qualitative systematic study
dc.creator.fl_str_mv Díaz-Martínez, Jorge L
Sanjay, Misra
Butt, Shariq Aziz
Ayeni, Foluso
dc.contributor.author.spa.fl_str_mv Díaz-Martínez, Jorge L
Sanjay, Misra
Butt, Shariq Aziz
Ayeni, Foluso
dc.subject.proposal.eng.fl_str_mv Agile methodology
Software development
Cost estimation techniques
topic Agile methodology
Software development
Cost estimation techniques
description Every competitive IT industry cannot avoid underestimating their projects’ effort, cost, and time. Some scrum project is completed delayed and undergoes difficulties due to over budgeting and a lack of needed functions. Software project failures are caused by incorrect and imprecise estimation; thus, it should be taken into account. A substantial change is required when Agile-based processes (e.g., Scrum) are introduced to the industry. The analysis is still difficult with Agile since requirements are constantly changing. Projects, individuals, and resistance issues, incorrect usage of cost factors, unawareness of regression testing work, readability of software requirements size as well as its related complexities, and so forth are all causes behind the difference in anticipated and real effort. This work analysis examined several publications and prospective researchers striving to narrow the actual and estimated effort gap. Decision-Based techniques significantly outperformed non-Decision Based and conventional estimating strategies by extensive literature analysis. We found that the regression test based estimation technique should be improved for accurate estimation of effort. However, scrum still needs a significant estimation technique to resolve the over budgeting issue. This study discussed the machine learning techniques, there proficiencies for estimation and flaws. The overall effort is the sum of all sprints components’ efforts, and it repeats after the prospective deliverable version.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-07-07T14:15:35Z
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dc.date.issued.none.fl_str_mv 2022-02-22
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dc.identifier.citation.spa.fl_str_mv Jorge-Martinez, D., Misra, S., Butt, S.A., Ayeni, F. (2022). Estimation Techniques for Scrum: A Qualitative Systematic Study. In: , et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_77
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identifier_str_mv Jorge-Martinez, D., Misra, S., Butt, S.A., Ayeni, F. (2022). Estimation Techniques for Scrum: A Qualitative Systematic Study. In: , et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_77
978-3-030-96298-2
10.1007/978-3-030-96299-9_77
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
978-3-030-96299-9
url https://hdl.handle.net/11323/9347
https://doi.org/10.1007/978-3-030-96299-9_77
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dc.relation.references.spa.fl_str_mv Steghöfer, J.P., Knauss, E., Alégroth, E., Hammouda, I., Burden, H., Ericsson, M.: Teaching agile-addressing the conflict between project delivery and application of agile methods. In: 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C), pp. 303–312. IEEE, May 2016
Martin, A., Anslow, C., Johnson, D.: Teaching agile methods to software engineering professionals: 10 years, 1000 release plans. In: Baumeister, H., Lichter, H., Riebisch, M. (eds.) Agile Processes in Software Engineering and Extreme Programming. XP 2017. LNBIP, vol. 283, pp. 151–166. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57633-6_10
Butt, S.A.: Study of agile methodology with the cloud. Pac. Sci. Rev. B Humanit. Soc. Sci. 2(1), 22–28 (2016)
Fuchs, C.: Adapting (to) agile methods: exploring the interplay of agile methods and organizational features (2019)
Przybyłek, A., Kotecka, D.: Making agile retrospectives more awesome. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1211–1216. IEEE, September 2017
Tessem, B.: The customer effect in agile system development projects. A process tracing case study. Procedia Comput. Sci. 121, 244–251 (2017)
Butt, S.A., Abbas, S.A., Ahsan, M.: Software development life cycle & software quality measuring types. Asian J. Math. Comput. Res. 11(2), 112–122 (2016)
Kim, S.I., Lee, J.Y.: Walk-Through screening center for COVID-19: an accessible and efficient screening system in a pandemic situation. J. Korean Med. Sci. 35(15), e154 (2020)
Janssen, M., van der Voort, H.: Agile and adaptive governance in crisis response: lessons from the COVID-19 pandemic. Int. J. Inf. Manag. 55, 102180 (2020)
Asare, A.O., Addo, P.C., Sarpong, E.O., Kotei, D.: COVID-19: optimizing business performance through agile business intelligence and data analytics. Open J. Bus. Manag. 8(5), 2071–2080 (2020)
Mishra, A., Misra, S.: People management in the software industry: the key to success. ACM SIGSOFT Softw. Eng. Notes 35(6), 1–4 (2010)
Fernández-Sanz, L., Gómez-Pérez, J., Diez-Folledo, T.I., Misra, S.: Researching human and organizational factors impact for decisions on software quality. In: Proceedings of the11th International Conference on Software Engineering and Applications, pp. 283–289 (2016)
Fernández-Sanz, L., Misra, S.: Influence of human factors in software quality and productivity. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) Computational Science and Its Applications - ICCSA 2011. LNCS, vol. 6786, pp. 257–269. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21934-4_22
Butt, S.A., Misra, S., Anjum, M.W., Hassan, S.A.: Agile project development issues during COVID-19. In: Przybyłek, A., Miler, J., Poth, A., Riel, A. (eds.) Lean and Agile Software Development. LASD 2021. LNBIP, vol. 408, pp. 59–70. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67084-9_4
Butt, S.A.: Analysis of unfair means cases in computer-based examination systems. Pac. Sci. Rev. B Humanit. Soc. Sci. 2(2), 75–79 (2016)
Przybyłek, A., Zakrzewski, M.: Adopting collaborative games into agile requirements engineering (2018)
Al Asheeri, M.M., Hammad, M.: Machine learning models for software cost estimation. In: 2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 1–6. IEEE, September 2019
Rao, C.P., Siva Kumar, P., Rama Sree, S., Devi, J.: An agile effort estimation based on story points using machine learning techniques. In: Bhateja, V., Tavares, J., Rani, B., Prasad, V., Raju, K. (eds.) Proceedings of the Second International Conference on Computational Intelligence and Informatics. AISC, vol. 712, pp. 209–219. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8228-3_20
Periyasamy, K., Chianelli, J.: A project tracking tool for scrum projects with machine learning support for cost estimation. In: EPiC Series in Computing, vol. 76, pp. 86–94 (2021)
Adnan, M., Afzal, M.: Ontology based multiagent effort estimation system for scrum agile method. IEEE Access 5, 25993–26005 (2017)
Kokol, P., Zagoranski, S., Kokol, M.: Software development with scrum: a bibliometric analysis and profile fi (2020)
Sharma, A., Chaudhary, N.: Linear regression model for agile software development effort estimation. In: 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–4. IEEE, December 2020
Syahputri, I.W., Ferdiana, R., Kusumawardani, S.S.: Does system based on decision based need software engineering method? Systematic review. In: 2020 Fifth International Conference on Informatics and Computing (ICIC), pp. 1–6. IEEE, November 2020
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spelling Díaz-Martínez, Jorge LSanjay, MisraButt, Shariq AzizAyeni, Foluso2022-07-07T14:15:35Z2022-07-07T14:15:35Z2022-02-22Jorge-Martinez, D., Misra, S., Butt, S.A., Ayeni, F. (2022). Estimation Techniques for Scrum: A Qualitative Systematic Study. In: , et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_77978-3-030-96298-2https://hdl.handle.net/11323/9347https://doi.org/10.1007/978-3-030-96299-9_7710.1007/978-3-030-96299-9_77Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/978-3-030-96299-9Every competitive IT industry cannot avoid underestimating their projects’ effort, cost, and time. Some scrum project is completed delayed and undergoes difficulties due to over budgeting and a lack of needed functions. Software project failures are caused by incorrect and imprecise estimation; thus, it should be taken into account. A substantial change is required when Agile-based processes (e.g., Scrum) are introduced to the industry. The analysis is still difficult with Agile since requirements are constantly changing. Projects, individuals, and resistance issues, incorrect usage of cost factors, unawareness of regression testing work, readability of software requirements size as well as its related complexities, and so forth are all causes behind the difference in anticipated and real effort. This work analysis examined several publications and prospective researchers striving to narrow the actual and estimated effort gap. Decision-Based techniques significantly outperformed non-Decision Based and conventional estimating strategies by extensive literature analysis. We found that the regression test based estimation technique should be improved for accurate estimation of effort. However, scrum still needs a significant estimation technique to resolve the over budgeting issue. This study discussed the machine learning techniques, there proficiencies for estimation and flaws. The overall effort is the sum of all sprints components’ efforts, and it repeats after the prospective deliverable version.application/pdfengSpringer International Publishing AGSwitzerlandInnovations in Bio-Inspired Computing and Applications;Lecture Notes in Networks and SystemsSteghöfer, J.P., Knauss, E., Alégroth, E., Hammouda, I., Burden, H., Ericsson, M.: Teaching agile-addressing the conflict between project delivery and application of agile methods. In: 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C), pp. 303–312. IEEE, May 2016Martin, A., Anslow, C., Johnson, D.: Teaching agile methods to software engineering professionals: 10 years, 1000 release plans. In: Baumeister, H., Lichter, H., Riebisch, M. (eds.) Agile Processes in Software Engineering and Extreme Programming. XP 2017. LNBIP, vol. 283, pp. 151–166. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57633-6_10Butt, S.A.: Study of agile methodology with the cloud. Pac. Sci. Rev. B Humanit. Soc. Sci. 2(1), 22–28 (2016)Fuchs, C.: Adapting (to) agile methods: exploring the interplay of agile methods and organizational features (2019)Przybyłek, A., Kotecka, D.: Making agile retrospectives more awesome. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1211–1216. IEEE, September 2017Tessem, B.: The customer effect in agile system development projects. A process tracing case study. Procedia Comput. Sci. 121, 244–251 (2017)Butt, S.A., Abbas, S.A., Ahsan, M.: Software development life cycle & software quality measuring types. Asian J. Math. Comput. Res. 11(2), 112–122 (2016)Kim, S.I., Lee, J.Y.: Walk-Through screening center for COVID-19: an accessible and efficient screening system in a pandemic situation. J. Korean Med. Sci. 35(15), e154 (2020)Janssen, M., van der Voort, H.: Agile and adaptive governance in crisis response: lessons from the COVID-19 pandemic. Int. J. Inf. Manag. 55, 102180 (2020)Asare, A.O., Addo, P.C., Sarpong, E.O., Kotei, D.: COVID-19: optimizing business performance through agile business intelligence and data analytics. Open J. Bus. Manag. 8(5), 2071–2080 (2020)Mishra, A., Misra, S.: People management in the software industry: the key to success. ACM SIGSOFT Softw. Eng. Notes 35(6), 1–4 (2010)Fernández-Sanz, L., Gómez-Pérez, J., Diez-Folledo, T.I., Misra, S.: Researching human and organizational factors impact for decisions on software quality. In: Proceedings of the11th International Conference on Software Engineering and Applications, pp. 283–289 (2016)Fernández-Sanz, L., Misra, S.: Influence of human factors in software quality and productivity. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) Computational Science and Its Applications - ICCSA 2011. LNCS, vol. 6786, pp. 257–269. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21934-4_22Butt, S.A., Misra, S., Anjum, M.W., Hassan, S.A.: Agile project development issues during COVID-19. In: Przybyłek, A., Miler, J., Poth, A., Riel, A. (eds.) Lean and Agile Software Development. LASD 2021. LNBIP, vol. 408, pp. 59–70. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67084-9_4Butt, S.A.: Analysis of unfair means cases in computer-based examination systems. Pac. Sci. Rev. B Humanit. Soc. Sci. 2(2), 75–79 (2016)Przybyłek, A., Zakrzewski, M.: Adopting collaborative games into agile requirements engineering (2018)Al Asheeri, M.M., Hammad, M.: Machine learning models for software cost estimation. In: 2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 1–6. IEEE, September 2019Rao, C.P., Siva Kumar, P., Rama Sree, S., Devi, J.: An agile effort estimation based on story points using machine learning techniques. In: Bhateja, V., Tavares, J., Rani, B., Prasad, V., Raju, K. (eds.) Proceedings of the Second International Conference on Computational Intelligence and Informatics. AISC, vol. 712, pp. 209–219. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8228-3_20Periyasamy, K., Chianelli, J.: A project tracking tool for scrum projects with machine learning support for cost estimation. In: EPiC Series in Computing, vol. 76, pp. 86–94 (2021)Adnan, M., Afzal, M.: Ontology based multiagent effort estimation system for scrum agile method. IEEE Access 5, 25993–26005 (2017)Kokol, P., Zagoranski, S., Kokol, M.: Software development with scrum: a bibliometric analysis and profile fi (2020)Sharma, A., Chaudhary, N.: Linear regression model for agile software development effort estimation. In: 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–4. IEEE, December 2020Syahputri, I.W., Ferdiana, R., Kusumawardani, S.S.: Does system based on decision based need software engineering method? Systematic review. In: 2020 Fifth International Conference on Informatics and Computing (ICIC), pp. 1–6. IEEE, November 2020829818© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbEstimation techniques for scrum: a qualitative systematic studyCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBhttp://purl.org/coar/version/c_b1a7d7d4d402bccehttps://link.springer.com/chapter/10.1007/978-3-030-96299-9_77#chapter-infoAgile methodologySoftware developmentCost estimation techniquesPublicationORIGINALEstimation techniques for scrum. a qualitative systematic study.pdfEstimation techniques for scrum. a qualitative systematic 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14:25:22.993https://creativecommons.org/licenses/by-nc-sa/4.0/© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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