Comprehensive Analysis of the Contributions of Machine Learning to Efficiency in Agile Project Management

Yadira Jazmin Perez Castillo, Sandra Dinora Orantes Jiménez, Patricio Orlando Letelier Torres

Abstract


Machine learning has driven efficiency in agile project management by increasing planning, risk identification, task assignment, and code quality improvement. These contributions have supported agile teams to work more effectively and meet deadlines, yet there remains untapped potential. Therefore, this paper proposes an analysis of these contributions to identify additional opportunities in this field, aiming to further enhance agile project management.

References


K. Schwaber and J. Sutherland, "La Guía Scrum. La Guía Definitiva de Scrum: Las Reglas del Juego," 2020.

Project Management Institute, "A Guide to the project management body of knowledge (PMBOK® guide)", 6th ed., Project Management Institute, 2017.

J. Sutherland, "Manifesto for Agile Software Development", 2022.

Project Management Institute, "Agile Practice Guide", Project Management Institute, 2017.

Digital.ai, "16th State of Agile Report Agile adoption accelerates across the enterprise," 2022.

D. Magaña Martínez and J. C. Fernandez-Rodriguez, "Artificial Intelligence Applied to Project Success: A Literature Review," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 3, no. 5, p. 77, 2015. DOI: 10.9781/ijimai.2015.3507

R. A. Santos, D. Vieira, A. Bravo, L. Suzuki, and F. Qudah, "A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?" J Softw Evol Proc. 2022. DOI: 10.1002/smr.2402

J. Pachouly, S. Ahirrao, K. Kotecha, G. Selvachandran, and A. Abraham, "A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools," Engineering Applications of Artificial Intelligence, vol. 111, pp. 104773, 2022. DOI: 10.1016/J.ENGAPPAI.2022.104773P

P. Sudarmaningtyas and R. Mohamed, A Review Article on Software Effort Estimation in Agile Methodology, vol. 29, Universiti Putra Malaysia Press, 2021, pp. 837-861.DOI: 10.47836/pjst.29.2.08

A. Jadhav, M. Kaur, and F. Akter, "Evolution of Software Development Effort and Cost Estimation Techniques: Five Decades Study Using Automated Text Mining Approach", Mathematical Problems in Engineering, vol. 2022, pp. 1-17, 2022. DOI:10.1155/2022/5782587

A. Sousa, J. P. Faria, and J. Mendes-Moreira, "An analysis of the state of the art of machine learning for risk assessment in software projects", Vols. 2021-July, Knowledge Systems Institute Graduate School, 2021, pp. 217-222. DOI: 10.18293/SEKE2021-097

M. Fernández-Diego, E. R. Méndez, F. González-Ladrón-De-Guevara, S. Abrahão, and E. Insfran, "An update on effort estimation in agile software development: A systematic literature review", vol. 8, Institute of Electrical and Electronics Engineers Inc., 2020, pp. 166768-166800.DOI: 10.1109/ACCESS.2020.3021664

P. Srivastava, N. Srivastava, R. Agarwal, and P. Singh, "Estimation in Agile Software Development Using Artificial Intelligence", vol. 376, Springer Science and Business Media Deutschland GmbH, 2022, pp. 83-93.DOI: 10.1007/978-981-16-8826-38

M. Arora, S. Verma, Kavita and S. Chopra, A Systematic Literature Review of Machine Learning Estimation Approaches in Scrum Projects, vol. 1040, 2020, pp. 573-586. DOI: 10.1007/978-981-15-1451-7_59

K. Periyasamy and J. Chianelli, "A Project Tracking Tool for Scrum Projects with Machine Learning Support for Cost Estimation", in 29th International Conference on Software Engineering and Data Engineering, vol 76, 2021, pp. 86-76. DOI: 10.29007/6vwh

M. A. Ramessur and S. D. Nagowah, "A predictive model to estimate effort in a sprint using machine learning techniques", vol. 13, Springer, 2021, pp. 1101-1110.DOI: 10.1007/s41870-021-00669-z

M. Vyas and N. Hemrajani, "Predicting effort of agile software projects using linear regression, ridge regression, and logistic regression", vol. 13, International Organization on 'Technical and Physical Problems of Engineering', 2021, pp. 14-19.

M. Abadeer and M. Sabetzadeh, "Machine Learning-based Estimation of Story Points in Agile Development: Industrial Experience and Lessons Learned," 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), Notre Dame, IN, USA, 2021, pp. 106-115.DOI: 10.1109/REW53955.2021.00022

Q. Bushra and A. Kadam, An improved technique for software cost estimations in agile software development using soft computing techniques, vol. 9, 2021.

T. Tekbulut, N. Canbaz and T. Ö. Kaya, "Machine Learning Application in LAPIS Agile Software Development Process," 2020 Turkish National Software Engineering Symposium (UYMS), Istanbul, Turkey, 2020, pp. 1-6, DOI: 10.1109/UYMS50627.2020.9247069.

D. Veido, B. Misnevs, and A. Plotkin, "The Method of Agile Projects Success Evaluation Using Machine Learning," in Lecture Notes in Networks and Systems, vol. 117, Springer, 2020, pp. 478-487. DOI: 10.1007/978-3-030-44610-9_47

R. Hanslo and M. Tanner, "Machine Learning models to predict Agile Methodology adoption", Institute of Electrical and Electronics Engineers Inc., 2020, pp. 697-704. DOI: 10.15439/2020F214

I. Shamshurin and J. S. Saltz, "A predictive model to identify Kanban teams at risk”, Multiagent and Grid Systems, vol. 14, pp. 321-335, 2019. DOI: 10.3233/MAS-190471

H. K. Dam, T. Tran, J. Grundy, A. Ghose, and Y. Kamei, "Towards Effective AI-Powered Agile Project Management," in Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE) - New Ideas and Emerging Results (NIER), 2019, pp. 41-44. DOI: 10.1109/ICSE-NIER.2019.00019


Full Text: PDF

Refbacks

  • There are currently no refbacks.