Agile Effort Estimation Using Machine Learning – A Systematic Review

Prabhneet kaur; Ankur sharma

doi:10.65301/iitm.2024.15.1.431

Articles Volume 15 Issue 1 January-June 2024

DOI 10.65301/iitm.2024.15.1.431

Agile Effort Estimation Using Machine Learning – A Systematic Review

Authors

227 Views

102 Downloads

Published 2024-01-30

Pages 119-137

Abstract

This review paper examines the integration of machine learning techniques into Agile software development, primarily focusing on effort estimation. It evaluates existing methodologies for Effort Prediction (EP) in Agile Software Development (ASD) projects, emphasizing the Evolutionary Cost-Sensitive Deep Belief Network (ECS-DBN) model’s ability to predict task effort during the early stages of Agile projects. The model’s efficacy is assessed using real world data from 160 tasks in Agile projects. Furthermore, the paper explores the applications of machine learning in various project management aspects within Scrum, such as sprint planning, backlog prioritization, and team performance prediction, as well as within Kanban, including workflow visualization, workload balancing, and lead time prediction. Emphasis is placed on the significance of data quality, algorithm selection, and the need for explainable AI. The paper concludes with a review of studies on software effort estimation in agile methodologies, highlighting the importance of machine learning algorithms in optimizing estimation formulas. Suggestions for future research include exploring additional metrics and applying machine learning techniques to industrial projects.

Keywords

Machine Learning Techniques Agile Software Development Effort Estimation Velocity Story Points Effort Prediction (EP) Evolutionary Cost-Sensitive Deep Belief Network (ECS-DBN) Real-World Data Sprint Planning Backlog Prioritization Team Performance Prediction Scrum Data Quality Algorithm Choice Explainable AI Swarm Optimization Algorithms Additional Metrics Industrial Projects

References

Usman, M., Mendes, E., Weidt, F., & Britto, R. Effort Estimation in Agile Software Development: A Systematic Literature Review. Department of Software Engineering, Blekinge Institute of Technology, Sweden; Federal University of Juiz de Fora, Brazil.
Khuat, T. T., & Le, M. H. A Novel Hybrid ABC-PSO Algorithm for Effort Estimation of Software Projects Using Agile Methodologies. DOI: 10.1515/jisys-2016-0294.
Satapathy, S. M., & Rath, S. K. Empirical Assessment of Machine Learning Models for Agile Software Development Effort Estimation Using Story Points. Innovations in Systems and Software Engineering. DOI: 10.1007/s11334-017-0288-z.
Satapathy, S. M., & Rath, S. K. (2017). Empirical Assessment of Machine Learning Models for Agile Software Development Effort Estimation Using Story Points. Innovations in Systems and Software Engineering, 13, 191–200.
Premalatha, H. M., & Srikrishna, C. V. (2019). Effort Estimation in Agile Software Development using Evolutionary Cost-Sensitive Deep Belief Network. International Journal of Intelligent Engineering and Systems, 12(2), 261.
Vyas, M., Bohra, A., Lamba, C. S., & Vyas, A. (2018). A Review on Software Cost and Effort Estimation Techniques for Agile Development Process. International Journal of Recent Research Aspects, 5(1), 1–5.
Radu, L. D. Effort Prediction in Agile Software Development with Bayesian Networks. Alexandru Ioan Cuza University of Iasi, Romania.
Cowlessur, S. K., Pattnaik, S., & Pattanayak, B. K. (2020). A Review of Machine Learning Techniques for Software Quality Prediction. Chapter in Book. DOI: 10.1007/978-981-15-1483-8_45.
Mendez, E., Guevara, F. G. L., Abrahão, S., & Insfran, E. (2020). An Update on Effort Estimation in Agile Software Development: A Systematic Literature Review. IEEE Access. DOI: 10.1109/ACCESS.2020.3021664.
Hanslo, R., & Tanner, M. Machine Learning Models to Predict Agile Methodology Adoption. Council for Scientific and Industrial Research, South Africa.
Vyas, M., & Hemrajani, N. (2021). Predicting Effort of Agile Software Projects using Linear Regression, Ridge Regression, and Logistic Regression. International Journal on Technical and Physical Problems of Engineering (IJTPE), Issue 47, Vol. 13(2).
Sudarmaningtyas, P., & Mohamed, R. (2021). A Review Article on Software Effort Estimation in Agile Methodology. Pertanika Journal of Science & Technology, 29(2), 837–861.
Alsaadi, B., & Saeedi, K. (2022). Data-driven Effort Estimation Techniques of Agile User Stories: A Systematic Literature Review. Artificial Intelligence Review. DOI: 10.1007/s10462-021-10132-x.
International Journal of Research and Analytical Reviews (IJRAR). (2022). An Experimental Study for Software Quality Prediction Using Machine Learning Methods. Volume 9, Issue 3.
Itzik, D., & Gelbard, R. (2023). Does Agile Methodology Fit All Characteristics of Software Projects? Review and Analysis. Empirical Software Engineering, 28:105.
Alazba, A., Aljamaan, H., & Alshayeb, M. (2023). Deep Learning Approaches for Bad Smell Detection: A Systematic Literature Review. Empirical Software Engineering, 28:77.
Uc-Cetina, V. (2023). Recent Advances in Software Effort Estimation using Machine Learning. Universidad Autónoma de Yucatán.
Sunda, N., & Sinha, R. R. (2023). Optimizing Effort Estimation in Agile Software Development: Traditional vs. Advanced ML Methods. IEEE ICCSAI.
Ilieva, R. Y., & Nikolov, M. A. (2019). The Impact of AI & ML in Agile Production. IEEE Electronica Conference.
Kumar, S., Arora, M., Sakshi, & Chopra, S. (2022). A Review of Effort Estimation in Agile Software Development using Machine Learning Techniques. IEEE ICIRCA.
Srikanth, B. V., Reddy, P. V. B., & Kamesh, D. B. K. (2023). Machine Learning Based Software Effort Estimation of Suggestive Agile and Scrumban Methodologies. Lecture Notes in Networks and Systems, Vol. 648.
Sinha, R. R., & Gora, R. K. (2020). Software Effort Estimation Using Machine Learning Techniques. Lecture Notes in Networks and Systems, Vol. 135.

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Prabhneet kaur, Ankur sharma . (2024). Agile Effort Estimation Using Machine Learning – A Systematic Review. IITM Journal of Management and IT, 15(1), 119-137.

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IITM Journal of Management and IT

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[1] Usman, M., Mendes, E., Weidt, F., & Britto, R. Effort Estimation in Agile Software Development: A Systematic Literature Review. Department of Software Engineering, Blekinge Institute of Technology, Sweden; Federal University of Juiz de Fora, Brazil.

[2] Khuat, T. T., & Le, M. H. A Novel Hybrid ABC-PSO Algorithm for Effort Estimation of Software Projects Using Agile Methodologies. DOI: 10.1515/jisys-2016-0294.

[3] Satapathy, S. M., & Rath, S. K. Empirical Assessment of Machine Learning Models for Agile Software Development Effort Estimation Using Story Points. Innovations in Systems and Software Engineering. DOI: 10.1007/s11334-017-0288-z.

[4] Satapathy, S. M., & Rath, S. K. (2017). Empirical Assessment of Machine Learning Models for Agile Software Development Effort Estimation Using Story Points. Innovations in Systems and Software Engineering, 13, 191–200.

[5] Premalatha, H. M., & Srikrishna, C. V. (2019). Effort Estimation in Agile Software Development using Evolutionary Cost-Sensitive Deep Belief Network. International Journal of Intelligent Engineering and Systems, 12(2), 261.

[6] Vyas, M., Bohra, A., Lamba, C. S., & Vyas, A. (2018). A Review on Software Cost and Effort Estimation Techniques for Agile Development Process. International Journal of Recent Research Aspects, 5(1), 1–5.

[7] Radu, L. D. Effort Prediction in Agile Software Development with Bayesian Networks. Alexandru Ioan Cuza University of Iasi, Romania.

[8] Cowlessur, S. K., Pattnaik, S., & Pattanayak, B. K. (2020). A Review of Machine Learning Techniques for Software Quality Prediction. Chapter in Book. DOI: 10.1007/978-981-15-1483-8_45.

[9] Mendez, E., Guevara, F. G. L., Abrahão, S., & Insfran, E. (2020). An Update on Effort Estimation in Agile Software Development: A Systematic Literature Review. IEEE Access. DOI: 10.1109/ACCESS.2020.3021664.

[10] Hanslo, R., & Tanner, M. Machine Learning Models to Predict Agile Methodology Adoption. Council for Scientific and Industrial Research, South Africa.

[11] Vyas, M., & Hemrajani, N. (2021). Predicting Effort of Agile Software Projects using Linear Regression, Ridge Regression, and Logistic Regression. International Journal on Technical and Physical Problems of Engineering (IJTPE), Issue 47, Vol. 13(2).

[12] Sudarmaningtyas, P., & Mohamed, R. (2021). A Review Article on Software Effort Estimation in Agile Methodology. Pertanika Journal of Science & Technology, 29(2), 837–861.

[13] Alsaadi, B., & Saeedi, K. (2022). Data-driven Effort Estimation Techniques of Agile User Stories: A Systematic Literature Review. Artificial Intelligence Review. DOI: 10.1007/s10462-021-10132-x.

[14] International Journal of Research and Analytical Reviews (IJRAR). (2022). An Experimental Study for Software Quality Prediction Using Machine Learning Methods. Volume 9, Issue 3.

[15] Itzik, D., & Gelbard, R. (2023). Does Agile Methodology Fit All Characteristics of Software Projects? Review and Analysis. Empirical Software Engineering, 28:105.

[16] Alazba, A., Aljamaan, H., & Alshayeb, M. (2023). Deep Learning Approaches for Bad Smell Detection: A Systematic Literature Review. Empirical Software Engineering, 28:77.

[17] Uc-Cetina, V. (2023). Recent Advances in Software Effort Estimation using Machine Learning. Universidad Autónoma de Yucatán.

[18] Sunda, N., & Sinha, R. R. (2023). Optimizing Effort Estimation in Agile Software Development: Traditional vs. Advanced ML Methods. IEEE ICCSAI.

[19] Ilieva, R. Y., & Nikolov, M. A. (2019). The Impact of AI & ML in Agile Production. IEEE Electronica Conference.

[20] Kumar, S., Arora, M., Sakshi, & Chopra, S. (2022). A Review of Effort Estimation in Agile Software Development using Machine Learning Techniques. IEEE ICIRCA.

[21] Srikanth, B. V., Reddy, P. V. B., & Kamesh, D. B. K. (2023). Machine Learning Based Software Effort Estimation of Suggestive Agile and Scrumban Methodologies. Lecture Notes in Networks and Systems, Vol. 648.

[22] Sinha, R. R., & Gora, R. K. (2020). Software Effort Estimation Using Machine Learning Techniques. Lecture Notes in Networks and Systems, Vol. 135.