A Review: Effort Estimation Model for Scrum Projects using Supervised Learning
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Abstract
Effort estimation practice in Agile is a critical component of the methodology to help cross-functional teams to plan and prioritize their work. Agile approaches have emerged in recent years as a more adaptable means of creating software projects because they consistently produce a workable end product that is developed progressively, preventing projects from failing entirely. Agile software development enables teams to collaborate directly with clients and swiftly adjust to changing requirements. This produces a result that is distinct, gradual, and targeted. It has been noted that the present Scrum estimate approach heavily relies on historical data from previous projects and expert opinion, while existing agile estimation methods like analogy and planning poker become unpredictable in the absence of historical data and experts. User Stories are used to estimate effort in the Agile approach, which has been adopted by 60–70% of the software businesses. This study's goal is to review a variety of strategies and techniques that will be used to gauge and forecast effort. Additionally, the supervised machine learning method most suited for predictive analysis is reviewed in this paper.
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Ramessur, M. A., & Nagowah, S. D. (2020). Factors affecting sprint effort estimation. Advances in Intelligent Systems and Computing, 507–518. https://doi.org/10.1007/978-981-15-1483-8_43
Ramessur, M. A., & Nagowah, S. D. (2021). A predictive model to estimate effort in a sprint using machine learning techniques. International Journal of Information Technology, 13(3), 1101–1110. https://doi.org/10.1007/s41870-021-00669-z
Abadeer, M., & Sabetzadeh, M. (2021). Machine learning-based estimation of story points in Agile Development: Industrial Experience and lessons learned. 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW). https://doi.org/10.1109/rew53955.2021.00022
Gultekin, M., & Kalipsiz, O. (2020). Story point-based effort estimation model with Machine Learning Techniques. International Journal of Software Engineering and Knowledge Engineering, 30(01), 43–66. https://doi.org/10.1142/s0218194020500035
Arora, M., Verma, S., & Chopra, S. (2020). A systematic literature review of machine learning estimation approaches in scrum projects. Cognitive Informatics and Soft Computing, 573-586.
Prasada Rao, C., Siva Kumar, P., Rama Sree, S., & Devi, J. (2018). An agile effort estimation based on story points using machine learning techniques. In Proceedings of the Second International Conference on Computational Intelligence and Informatics (pp. 209-219). Springer, Singapore.
Sharma, A., Ranjan, R.: Software effort estimation using neuro fuzzy inference system: past and present. Int. J. Recent Innov. Trends Comput. Commun. 5(8), 78–83 (2017)
Samareh Moosavi, S.H., Khatibi Bardsiri, V.: Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng. Appl. Artif. Intell. 60, 1–15 (2017)
Pospieszny, P., Czarnacka-Chrobot, B., Kobylinski, A.: An effective approach for software project effort and duration estimation with machine learning algorithms. J. Syst. Softw. 137, 184–196 (2018)
Azzeh, M., Nassif, A.B., Banitaan, S.: Comparative analysis of soft computing techniques for predicting software effort based use case points. IET Softw. 12(1), 19–29 (2018)
Yousef, Q.M., Alshaer, Y.A.: Dragonfly estimator: a hybrid software projects’ efforts estimation model using artificial neural network and Dragonfly algorithm. Int. J. Comput. Sci. Netw. Secur. 17(9), 108–120 (2017)
Menzies, T., Yang, Y., Mathew, G., Boehm, B., Hihn, J.: Negative results for software effort estimation. Empir. Softw. Eng. 22(5), 2658–2683 (2017)
Alostad, J.M., Abdullah, L.R.A., Aali, L.S.: A fuzzy based model for effort estimation in Scrum projects. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8(9), 270–277 (2017)
Bilgaiyan, S., Sagnika, S., Mishra, S., Das, M.: A systematic review on software cost estimation in Agile software development. J. Eng. Sci. Technol. Rev. 10(4), 51–64 (2017)
Dragicevic, S., Celar, S., Turic, M.: Bayesian network model for task effort estimation in agile software development. J. Syst. Softw. 127, 109–119 (2017)
Soren, N. ., & Selvi Rajendran , P. . (2023). Artificial Intelligence Based - Oryza Sativa Leaf Ailment Recognition using DCT with Deep NN. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 242–253. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2650
Salmanoglu, O.D.M., Hacaloglu, T.: Effort estimation for Agile software development : com- parative case studies using COSMIC functional size measurement and story points. In: ACM Mensura, pp. 1–9 (2017)
Padmaja, M., Haritha, D.: Software effort estimation using meta heuristic algorithm. Int. J. Adv. Res. Comput. Sci. 8(5), 196–201 (2017)
Murillo-Morera, J., Quesada-López, C., Castro-Herrera, C., Jenkins, M.: A genetic algorithm based framework for software effort prediction. J. Softw. Eng. Res. Dev. 5(1), 1–33 (2017)
de Araújo, R.A., Oliveira, A.L.I., Meira, S.: A class of hybrid multilayer perceptrons for software development effort estimation problems. Expert Syst. Appl. 90, 1–12 (2017)
Khuat, T., Le, H.: An effort estimation approach for Agile software development using fireworks algorithm optimized neural network. Int. J. Comput. Sci. Inf. Secur. 14(7), 122–130 (2018)
Adnan, M., Afzal, M.: Ontology based multiagent effort estimation system for Scrum Agile method. IEEE Access, 25993–26005 (2017)
Satapathy, S.M., Rath, S.K.: Empirical assessment of machine learning models for agile software development effort estimation using story points. Innov. Syst. Softw. Eng. 13(2–3), 191–200 (2017)
Prof. Nitin Sherje. (2017). Phase Shifters with Tunable Reflective Method Using Inductive Coupled Lines. International Journal of New Practices in Management and Engineering, 6(01), 08 - 13. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/50
Khuat, T.T., Le, M.H.: A novel hybrid ABC-PSO algorithm for effort estimation of software projects using Agile methodologies. J. Intell. Syst. 27(3), 489–506 (2018)
Choetkiertikul, M., Dam, H.K., Tran, T., Pham, T.T.M., Ghose, A., Menzies, T.: A deep learning model for estimating story points. IEEE Trans. Softw. Eng. 14(8), 1–12 (2016).
A. Przybylek, D. Kotecka, “Making agile retrospectives more awesome,” In Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic, 2017. https://doi.org/10.15439/2017F423
A. Przybylek, M. Zakrzewski, “Adopting Collaborative Games into Agile Requirements Engineering,” In 13th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE'18), Funchal, Madeira, Portugal, 2018. https://doi.org/10.5220/0006681900540064
A. Przyby?ek, M. Olszewski, "Adopting collaborative games into Open Kanban," In Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland, 2016. https://doi.org/10.15439/2016F509
Ahammad, D. S. K. H. (2022). Microarray Cancer Classification with Stacked Classifier in Machine Learning Integrated Grid L1-Regulated Feature Selection. Machine Learning Applications in Engineering Education and Management, 2(1), 01–10. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/18
K. Schwaber and J. Sutherland, “The Scrum Guide,” scrum.org, https://www.scrum.org/index.php/resources/scrum-guide. 2020.
CollabNet VersionOne, “13th Annual State of Agile Report,” collab.net, https://www.stateofagile.com. 2020.
A. Jung, “Machine Learning: Basic Principles,” arXiv: 1805.05052v11[cs.LG], https://arxiv.org/pdf/1805.05052.pdf. 2019.
The Standish Group, “CHAOS Report 2015,” The Standish Group, https://www.standishgroup.com/sample_research_files/CHAOSRe port2015-Final.pdf. 2020.
Vitality Chicago, “Agile Project Success Rates are 2X Higher than Traditional Projects,” VitalityChicago https://vitalitychicago.com/blog/agile-projects-are-more- successful-traditional-projects/. 2020.
J. Kahles, J. Torronen, T. Huuhtanen and A. Jung, “Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments,” In Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation, ICST 2019, pp. 379-390. [8730163] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICST.2019.00047
S.M. Satapathy and S.K. Rath, “Empirical assessment of machine learning models for agile software development effort estimation using story points,” Innovations in Systems and Software Engineering, 13(2-3), pp.191-200. 2017.
L. Butgereit, 2019, “Using Machine Learning to Prioritize Automated Testing in an Agile Environment,” In 2019 Conference on Information Communications Technology and Society (ICTAS), pp. 1-6. IEEE. 2019.
R. Hanslo and E. Mnkandla, “Scrum Adoption Challenges Detection Model: SACDM,” In Federated Conference on Computer Science and Information Systems (FedCSIS), Poznan, Poland: IEEE: 949–957. 2018.
R. Hanslo, E. Mnkandla and A. Vahed, “Factors that contribute significantly to Scrum adoption,” In Federated Conference on Computer Science and Information Systems (FedCSIS), Leipzig, Germany: IEEE: 821–829. 2019.
R. Hanslo, A. Vahed and E. Mnkandla, “Quantitative Analysis of the Scrum Framework,” In: Przyby?ek A., Morales-Trujillo M. (eds) Advances in Agile and User-Centred Software Engineering. LASD 2019, MIDI 2019, Lecture Notes in Business Information Processing, vol 376. Springer, Cham. 2020. https://doi.org/10.1007/978-3-030-37534-8_5
A. Gelman, B. Goodrich, J. Gabry and A. Vehtari, “R-squared for Bayesian regression models,” The American Statistician, 73(3), pp.307-309. 2019.
Bhawana Verma, S. K.A. (2019). Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application. International Journal on Recent and Innovation Trends in Computing and Communication, 7(1), 27–34. https://doi.org/10.17762/ijritcc.v7i1.5222
S. K.A., Raj, A. ., Sharma, V., & Kumar, V. (2022). Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language using CNN. International Journal on Recent and Innovation Trends in Computing and Communication, 10(4), 10–14. https://doi.org/10.17762/ijritcc.v10i4.5556.