Prediction of a Sprint Deliverys Capabilities in Iterative-based Software Development

Clements Enrico Bramantyo Hady
Dana Sulistyo Kusumo


DOI: https://doi.org/10.29100/jipi.v8i1.3292

Abstract


Iterative-based software development has been frequently implemented in working environment. A modern era software project demands that the product is delivered on every sprint development. Hence, the execution of a sprint requires ample supervision and capabilities to deliver a high quality product at the end of the software project development. This researchs purpose is to give support for a software projects supervisor or owner in predicting the end products capability by knowing the performance level of each sprint. The method proposed for this purpose is to build a prediction model utilizing a number of features in a form of characteristics from a dataset containing software project iterations. The proposed model is built using Random Forest Regressor as a main method, with KNN (K-Nearest Neighbors) and Decision Tree Regressor being the comparison methods. Testing results show that compared to KNN and Decision Tree, Random Forest Regressor yields the best performance through its steady results on every stage progression of all tested software projects.

Keywords


prediction model, software development, sprint

Full Text:

PDF

Article Metrics :

References


L. Gonalves, Scrum, Control. Manag. Rev., vol. 62, no. 4, pp. 4042, May 2018, doi: 10.1007/s12176-018-0020-3.

F. Hayat, A. U. Rehman, K. S. Arif, K. Wahab, and M. Abbas, The Influence of Agile Methodology (Scrum) on Software Project Management, Proc. - 20th IEEE/ACIS Int. Conf. Softw. Eng. Artif. Intell. Netw. Parallel/Distributed Comput. SNPD 2019, pp. 145149, Jul. 2019, doi: 10.1109/SNPD.2019.8935813.

J. Wright, Scrum: the complete guide to the agile project management framework that helps the software development lean team to efficiently structure and simplify the work & solve problems in half the time. p. 95, 2020.

M. Hron and N. Obwegeser, Why and how is Scrum being adapted in practice: A systematic review, J. Syst. Softw., vol. 183, p. 111110, Jan. 2022, doi: 10.1016/J.JSS.2021.111110.

M. Hron and N. Obwegeser, Scrum in practice: An overview of Scrum adaptations, Proc. Annu. Hawaii Int. Conf. Syst. Sci., vol. 2018-Janua, pp. 54455454, 2018, doi: 10.24251/hicss.2018.679.

M. Marinho, J. Noll, and S. Beecham, Uncertainty management for global software development teams, Proc. - 2018 Int. Conf. Qual. Inf. Commun. Technol. QUATIC 2018, pp. 238246, Dec. 2018, doi: 10.1109/QUATIC.2018.00042.

M. Choetkiertikul, H. K. Dam, T. Tran, A. Ghose, and J. Grundy, Predicting Delivery Capability in Iterative Software Development, IEEE Trans. Softw. Eng., vol. 44, no. 6, pp. 551573, 2018, doi: 10.1109/TSE.2017.2693989.

M. Choetkiertikul, H. K. Dam, T. Tran, and A. Ghose, Predicting delays in software projects using networked classification, Proc. - 2015 30th IEEE/ACM Int. Conf. Autom. Softw. Eng. ASE 2015, pp. 353364, 2016, doi: 10.1109/ASE.2015.55.

C. Verwijs and D. Russo, A Theory of Scrum Team Effectiveness. 2021. [Online]. Available: http://arxiv.org/abs/2105.12439

P. Pospieszny, B. Czarnacka-Chrobot, and A. Kobylinski, An effective approach for software project effort and duration estimation with machine learning algorithms, J. Syst. Softw., vol. 137, pp. 184196, Mar. 2018, doi: 10.1016/J.JSS.2017.11.066.

M. Choetkiertikul, H. K. Dam, T. Tran, T. Pham, A. Ghose, and T. Menzies, A Deep Learning Model for Estimating Story Points, IEEE Trans. Softw. Eng., vol. 45, no. 7, pp. 637656, 2019, doi: 10.1109/TSE.2018.2792473.

P. Ardimento and C. Mele, Using BERT to Predict Bug-Fixing Time, IEEE Conf. Evol. Adapt. Intell. Syst., vol. 2020-May, May 2020, doi: 10.1109/EAIS48028.2020.9122781.

M. Choetkiertikul, H. K. Dam, T. Tran, and A. Ghose, Characterization and prediction of issue-related risks in software projects, IEEE Int. Work. Conf. Min. Softw. Repos., vol. 2015-Augus, pp. 280291, 2015, doi: 10.1109/MSR.2015.33.

D. Denisko and M. M. Hoffman, Classification and interaction in random forests, Proc. Natl. Acad. Sci. U. S. A., vol. 115, no. 8, pp. 16901692, Feb. 2018, doi: 10.1073/PNAS.1800256115.

O. Sagi and L. Rokach, Ensemble learning: A survey, Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 8, no. 4, p. e1249, Jul. 2018, doi: 10.1002/WIDM.1249.

S. Zhang, M. Zong, X. Zhu, D. Cheng, and X. Li, Learning k for kNN classifi-cation, ACM Trans. Intell. Syst. Technol, vol. 8, no. 43, 2017, doi: 10.1145/2990508.

H. Al-Shehri et al., Student performance prediction using Support Vector Machine and K-Nearest Neighbor, Can. Conf. Electr. Comput. Eng., Jun. 2017, doi: 10.1109/CCECE.2017.7946847.

R. Hasan, S. Palaniappan, A. R. A. Raziff, S. Mahmood, and K. U. Sarker, Student Academic Performance Prediction by using Decision Tree Algorithm, 2018 4th Int. Conf. Comput. Inf. Sci. Revolutionising Digit. Landsc. Sustain. Smart Soc. ICCOINS 2018 - Proc., Oct. 2018, doi: 10.1109/ICCOINS.2018.8510600.

A. K. Hamoud, A. S. Hashim, and W. A. Awadh, Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis, Int. J. Interact. Multimed. Artif. Intell., vol. 5, no. 2, p. 26, 2018, doi: 10.9781/ijimai.2018.02.004.


slot gacor slot