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Machine Learning models to predict Agile Methodology adoption

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
Języki publikacji
EN
Abstrakty
EN
Agile software development methodologies are used in many industries of the global economy. The Scrum framework is the predominant Agile methodology used to develop, deliver, and maintain complex products. While the success of software projects has significantly improved while using Agile methodologies in comparison to the Waterfall methodology, a large proportion of projects continue to be challenged or fails. The primary objective of this paper is to use machine learning to develop predictive models for Scrum adoption, identifying a preliminary model with the highest prediction accuracy. The machine learning models were implemented using multiple linear regression statistical techniques. In particular, a full feature set adoption model, a transformed logarithmic adoption model, and a transformed logarithmic with omitted features adoption model were evaluated for prediction accuracy. Future research could improve upon these findings by incorporating additional model evaluation and validation techniques.
Rocznik
Tom
Strony
697--704
Opis fizyczny
Bibliogr. 24 poz., wykr., tab.
Twórcy
  • Council for Scientific and Industrial Research, South Africa. Department of Information Systems, University of Cape Town, South Africa
  • Department of Information Systems, University of Cape Town, South Africa
Bibliografia
  • 1. 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
  • 2. 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
  • 3. A. Przybyłek, 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
  • 4. K. Schwaber and J. Sutherland, “The Scrum Guide,” scrum.org, https://www.scrum.org/index.php/resources/scrum-guide. 2020.
  • 5. CollabNet VersionOne, “13th Annual State of Agile Report,” collab.net, https://www.stateofagile.com. 2020.
  • 6. A. Jung, “Machine Learning: Basic Principles,” https://arxiv.org/abs/ 1805.05052v11[cs.LG], https://arxiv.org/pdf/1805.05052.pdf. 2019.
  • 7. J. Schleier-Smith, “An architecture for agile machine learning in real-time applications,” In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2059-2068, ACM, 2015.
  • 8. R. Hoda and L.K. Murugesan, “Multi-level agile project management challenges: A self-organizing team perspective,” The Journal of Systems & Software, 117, 245-257. 2016. https://doi.org/10.1016/j.jss.2016.02.049
  • 9. The Standish Group, “CHAOS Report 2015,” The Standish Group, https://www.standishgroup.com/sample_research_files/CHAOSReport2015-Final.pdf. 2020.
  • 10. 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.
  • 11. 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
  • 12. K. Moharreri, A.V. Sapre, J. Ramanathan and R. Ramnath, “Cost-effective supervised learning models for software effort estimation in agile environments,” In 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 135-140. IEEE. 2016.
  • 13. 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.
  • 14. H.M. Chen, R. Kazman and S. Haziyev, “Agile big data analytics for web-based systems: An architecture-centric approach,” IEEE Transactions on Big Data, 2(3), pp.234-248. 2016.
  • 15. 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.
  • 16. 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.
  • 17. F. Sultan and L. Chan, “The adoption of new technology: the case of object-oriented computing in software companies,” IEEE transactions on Engineering Management, 47(1): 106–126. 2000.
  • 18. 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.
  • 19. R. Hanslo, A. Vahed and E. Mnkandla, “Quantitative Analysis of the Scrum Framework,” In: Przybyłek 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
  • 20. S. Chen and P. Gopalakrishnan, “Speaker, environment and channel change detection and clustering via the bayesian information criterion,” In Proc. DARPA broadcast news transcription and understanding workshop, vol. 8, pp. 127-132. Feb. 1998.
  • 21. D.L. Weakliem, “A critique of the Bayesian information criterion for model selection,” Sociological Methods & Research, 27(3), pp.359-397. 1999.
  • 22. A. Gelman, B. Goodrich, J. Gabry and A. Vehtari, “R-squared for Bayesian regression models,” The American Statistician, 73(3), pp.307-309. 2019.
  • 23. D.M. Allen, “Mean square error of prediction as a criterion for selecting variables,” Technometrics, 13(3), pp.469-475. 1971.
  • 24. C. Tantithamthavorn, S. McIntosh, A. E. Hassan and K. Matsumoto, “An Empirical Comparison of Model Validation Techniques for Defect Prediction Models,” In IEEE Transactions on Software Engineering, vol. 43, no. 1, pp. 1-18, Jan. 2017. https://doi.org/10.1109/TSE.2016.2584050
Uwagi
1. Track 5: Software and System Engineering
2. Technical Session: 4th International Conference on Lean and Agile Software Development
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6b8bea3a-2674-4f98-90c9-dfa45d472987
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