A Cost Estimating Method for Agile Software Development
In every software development project, the software effort estimating procedure is an important process in software engineering and always critical. The consistency of effort and timeline estimation, along with several factors, determines whether a project succeeds or fails. Both academics and profe...
- Autores:
-
Aziz Butt, Shariq
Misra, Sanjay
Piñeres-Espitia, Gabriel
Ariza-Colpas, Paola
Mohan Sharma, Mayank
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8816
- Acceso en línea:
- https://hdl.handle.net/11323/8816
https://doi.org/10.1007/978-3-030-87007-2_17
https://repositorio.cuc.edu.co/
- Palabra clave:
- Software effort estimate
Agile development
User stories
Metrics and measurement
Maintainability
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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A Cost Estimating Method for Agile Software Development |
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A Cost Estimating Method for Agile Software Development |
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A Cost Estimating Method for Agile Software Development Software effort estimate Agile development User stories Metrics and measurement Maintainability |
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A Cost Estimating Method for Agile Software Development |
| title_full |
A Cost Estimating Method for Agile Software Development |
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A Cost Estimating Method for Agile Software Development |
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A Cost Estimating Method for Agile Software Development |
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A Cost Estimating Method for Agile Software Development |
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Aziz Butt, Shariq Misra, Sanjay Piñeres-Espitia, Gabriel Ariza-Colpas, Paola Mohan Sharma, Mayank |
| dc.contributor.author.spa.fl_str_mv |
Aziz Butt, Shariq Misra, Sanjay Piñeres-Espitia, Gabriel Ariza-Colpas, Paola Mohan Sharma, Mayank |
| dc.subject.spa.fl_str_mv |
Software effort estimate Agile development User stories Metrics and measurement Maintainability |
| topic |
Software effort estimate Agile development User stories Metrics and measurement Maintainability |
| description |
In every software development project, the software effort estimating procedure is an important process in software engineering and always critical. The consistency of effort and timeline estimation, along with several factors, determines whether a project succeeds or fails. Both academics and professionals worked on the estimation approaches in software engineering. But, all these approaches have many problems that need to be addressed. One of the most difficult aspects of software engineering is estimating effort in agile development. This study aims to provide an effort estimation method for agile software development projects. Because in software engineering, the agile method is widely used for the development of software applications. The development and usage of the agile method are described in depth in this study. The framework is configured with empirical data gathered by projects from the software industry. The test findings reveal that the estimation method has great estimation accuracy in respect of mean magnitude of relative error (MMRE) and Prediction of Error PRED (n). The suggested approach achieves more accuracy for effort estimation as compare to others. |
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2021 |
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2021-10-29T13:03:39Z |
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2021 |
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https://doi.org/10.1007/978-3-030-87007-2_17 |
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Corporación Universidad de la Costa |
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eng |
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Popli, R., Chauhan, N.: Sprint-point based estimation in scrum In: Proceedings of IEEE Conference, GLA University, Mathura, 9–10 March 2013 Bhalereo, S., Ingle, M.: Incorporating vital factors in agile estimation through algorithmic methods Int. J. Comput. Sci. Appl. Technomath. Res. Foundat. 6(1) 85–97 (2009) Misra, S., Omorodion, F.M., Damasevicius, R.: Metrics for measuring progress and productivity in agile software development. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds.) IBICA 2020. AISC, vol. 1372, pp. 469–478. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73603-3_44 Attarzadeh, I., Hock Ow, S.: Software development effort estimation based on a new fuzzy logic model. Int. J. Comput. Theory Eng. 1, 1793–8201 (2009) Butt, S.A., Misra, S., Anjum, M.W., Hassan, S.A.: Agile project development issues during COVID-19. In: International Conference on Lean and Agile Software Development, pp. 59–70. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67084-9_4 Misra, S.: Pair programming: an empirical investigation in an agile software development environment. In: Przybyłek, A., Miler, J., Poth, A., Riel, A. (eds.) LASD 2021. LNBIP, vol. 408, pp. 195–199. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67084-9_13 Abioye, T.E., Arogundade, O.T., Misra, S., Akinwale, A.T., Adeniran, O.J.: Toward ontology‐based risk management framework for software projects: an empirical study. J. Softw. Evol. Process 32(12), e2269 (2020) Rimal, Y., Pandit, P., Gocchait, S., Butt, S.A., Obaid, A.J.: Hyperparameter determines the best learning curve on single, multi-layer and deep neural network of student grade prediction of Pokhara University Nepal. J. Phys. Conf. Ser. 1804(1), 012054 (2021). IOP Publishing 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) Butt, S.A., Gochhait, S., Andleeb, S., Adeel, M.: Games features for health disciplines for patient learning as entertainment. In: Digital Entertainment, pp. 65–86. Palgrave Macmillan, Singapore (2021). 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 Behera, R.K., Jena, M., Rath, S.K., Misra, S.: Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Inf. Process. Manage. 58(1), 102435 (2021) Kumari, A., Behera, R.K., Sahoo, K.S., Nayyar, A., Kumar Luhach, A., Prakash Sahoo, S.: Supervised link prediction using structured‐based feature extraction in social network. Concurrency Comput. Pract. Exp. e5839 (2020) Anusuya, V., Gomathi, V.: An efficient technique for disease prediction by using enhanced machine learning algorithms for categorical medical dataset. Inf. Technol. Control 50(1), 102–122 (2021) Behera, R.K., Shukla, S., Rath, S.K., Misra, S.: Software reliability assessment using machine learning technique. In: International Conference on Computational Science and Its Applications, pp. 403–411. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95174-4_32 Arogundade, O.T., Atasie, C. Misra, S., Sakpere, A.B., Abayomi-Alli, O.O., Adesemowo K.A.: Improved predictive system for soil test fertility performance using fuzzy rule approach. In: Soft Computing and Its Engineering Applications: Second International Conference, IcSoftComp 2020, Changa, Anand, India, 11–12 December 2020, Proceedings, vol. 1374, p. 249. Springer, Cham (2021). https://doi.org/10.1007/978-981-16-0708-0_21 Butt, S.A.: Study of agile methodology with the cloud. Pacific Sci. Rev. B Human. Soc. Sci. 2(1), 22–28 (2016) |
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Aziz Butt, ShariqMisra, SanjayPiñeres-Espitia, GabrielAriza-Colpas, PaolaMohan Sharma, Mayank2021-10-29T13:03:39Z2021-10-29T13:03:39Z2021https://hdl.handle.net/11323/8816https://doi.org/10.1007/978-3-030-87007-2_17Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In every software development project, the software effort estimating procedure is an important process in software engineering and always critical. The consistency of effort and timeline estimation, along with several factors, determines whether a project succeeds or fails. Both academics and professionals worked on the estimation approaches in software engineering. But, all these approaches have many problems that need to be addressed. One of the most difficult aspects of software engineering is estimating effort in agile development. This study aims to provide an effort estimation method for agile software development projects. Because in software engineering, the agile method is widely used for the development of software applications. The development and usage of the agile method are described in depth in this study. The framework is configured with empirical data gathered by projects from the software industry. The test findings reveal that the estimation method has great estimation accuracy in respect of mean magnitude of relative error (MMRE) and Prediction of Error PRED (n). The suggested approach achieves more accuracy for effort estimation as compare to others.Aziz Butt, ShariqMisra, SanjayPiñeres-Espitia, GabrielAriza-Colpas, PaolaMohan Sharma, Mayankapplication/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2International Conference on Computational Science and Its Applicationshttps://link.springer.com/chapter/10.1007/978-3-030-87007-2_17Software effort estimateAgile developmentUser storiesMetrics and measurementMaintainabilityA Cost Estimating Method for Agile Software DevelopmentArtículo 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/acceptedVersionPopli, R., Chauhan, N.: Sprint-point based estimation in scrum In: Proceedings of IEEE Conference, GLA University, Mathura, 9–10 March 2013Bhalereo, S., Ingle, M.: Incorporating vital factors in agile estimation through algorithmic methods Int. J. Comput. Sci. Appl. Technomath. Res. Foundat. 6(1) 85–97 (2009)Misra, S., Omorodion, F.M., Damasevicius, R.: Metrics for measuring progress and productivity in agile software development. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds.) IBICA 2020. AISC, vol. 1372, pp. 469–478. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73603-3_44Attarzadeh, I., Hock Ow, S.: Software development effort estimation based on a new fuzzy logic model. Int. J. Comput. Theory Eng. 1, 1793–8201 (2009)Butt, S.A., Misra, S., Anjum, M.W., Hassan, S.A.: Agile project development issues during COVID-19. In: International Conference on Lean and Agile Software Development, pp. 59–70. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67084-9_4Misra, S.: Pair programming: an empirical investigation in an agile software development environment. In: Przybyłek, A., Miler, J., Poth, A., Riel, A. (eds.) LASD 2021. LNBIP, vol. 408, pp. 195–199. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67084-9_13Abioye, T.E., Arogundade, O.T., Misra, S., Akinwale, A.T., Adeniran, O.J.: Toward ontology‐based risk management framework for software projects: an empirical study. J. Softw. Evol. Process 32(12), e2269 (2020)Rimal, Y., Pandit, P., Gocchait, S., Butt, S.A., Obaid, A.J.: Hyperparameter determines the best learning curve on single, multi-layer and deep neural network of student grade prediction of Pokhara University Nepal. J. Phys. Conf. Ser. 1804(1), 012054 (2021). IOP PublishingButt, 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)Butt, S.A., Gochhait, S., Andleeb, S., Adeel, M.: Games features for health disciplines for patient learning as entertainment. In: Digital Entertainment, pp. 65–86. Palgrave Macmillan, Singapore (2021).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 2017Behera, R.K., Jena, M., Rath, S.K., Misra, S.: Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Inf. Process. Manage. 58(1), 102435 (2021)Kumari, A., Behera, R.K., Sahoo, K.S., Nayyar, A., Kumar Luhach, A., Prakash Sahoo, S.: Supervised link prediction using structured‐based feature extraction in social network. Concurrency Comput. Pract. Exp. e5839 (2020)Anusuya, V., Gomathi, V.: An efficient technique for disease prediction by using enhanced machine learning algorithms for categorical medical dataset. Inf. Technol. Control 50(1), 102–122 (2021)Behera, R.K., Shukla, S., Rath, S.K., Misra, S.: Software reliability assessment using machine learning technique. In: International Conference on Computational Science and Its Applications, pp. 403–411. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95174-4_32Arogundade, O.T., Atasie, C. Misra, S., Sakpere, A.B., Abayomi-Alli, O.O., Adesemowo K.A.: Improved predictive system for soil test fertility performance using fuzzy rule approach. In: Soft Computing and Its Engineering Applications: Second International Conference, IcSoftComp 2020, Changa, Anand, India, 11–12 December 2020, Proceedings, vol. 1374, p. 249. Springer, Cham (2021). https://doi.org/10.1007/978-981-16-0708-0_21Butt, S.A.: Study of agile methodology with the cloud. Pacific Sci. Rev. B Human. Soc. 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