Abstract
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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References
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)
Przybylek, A., Kowalski, W.: Utilizing online collaborative games to facilitate agile software development. In: 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 811–815. IEEE, September 2018
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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Butt, S.A., Misra, S., Piñeres-Espitia, G., Ariza-Colpas, P., Sharma, M.M. (2021). A Cost Estimating Method for Agile Software Development. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_17
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