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Pertanika Journal of Science & Technology

Official journal of Universiti Putra Malaysia for scholarly work across science, engineering and related technologies.

e-ISSN 2231-8526 ISSN 0128-7680
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Research article

Significant Factors in Agile Software Development of Effort Estimation

Pantjawati Sudarmaningtyas and Rozlina Mohamed

Volume 30, Issue 4 October 2022 Published 28 September 2022
https://doi.org/10.47836/pjst.30.4.30
KeywordsAgile methodology, effort estimation, path analysis, people-related factors, project-related factors, structural equation model
Article content

Abstract

The Agile effort estimation involves project-related and people-related factors. This research objective is to find the factors that influence Agile effort estimation significantly through path analysis using a structural equation model. This research built an agile effort estimation path coefficient model from six constructs from theories and previous studies. Project-related factors represent by requirement and design implementation constructs. People-related factors are measured by the construct of experience, knowledge, and technical ability. The last construct is the effort itself. SmartPLS is employed for the confirmatory composite analysis and the structural model assessment. The confirmatory composite analysis indicated that all constructs are reliable and valid. Furthermore, the structural model assessment found that all factors of project-related constructs have a positive relationship and significant influence, showing a coefficient path value of 59.1% between requirement and design implementation constructs. All constructs represent people-related factors indicated by the coefficient path value of 67% between experience and knowledge, 42.6% between experience and technical ability, and 54.4% between knowledge and technical ability. In addition, all constructs proved influential simultaneously to effort by 31.1%. Positively contribute provided by requirement, experience, and technology’s ability. Significantly influenced provided by constructs of the developer’s knowledge and technical ability. The largest effect is given by technical ability, knowledge, and experience on medium and small scales. Contrarily, both constructs from project-related effects can be negligible because there was no influence. Based on the result, this study concludes that the significant factors in Agile effort estimation are technical ability, knowledge, and experience.
Supporting literature

References

  1. Abrahamsson, P., Salo, O., Ronkainen, J., & Warsta, J. (2002). Agile software development methods: Review and analysis. arXiv Preprint. http://arxiv.org/abs/1709.08439
  2. Adnan, M., & Afzal, M. (2017). Ontology based multiagent effort estimation system for scrum agile method. IEEE Access, 5, 25993-26005. https://doi.org/10.1109/ACCESS.2017.2771257
  3. Asnawi, A. L., Gravell, A. M., & Wills, G. B. (2012). Factor analysis: Investigating important aspects for agile adoption in Malaysia. In 2012 Agile India (pp. 60-63). IEEE Publishing. https://doi.org/10.1109/AgileIndia.2012.13
  4. Bloch, M., Blumberg, S., & Laartz, J. (2012). Delivering Large Scale IT.pdf. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/delivering-large-scale-it-projects-on-time-on-budget-and-on-value
  5. Boehm, B. (2006). A view of 20th and 21st century software engineering. In Proceeding of the 28th International Conference on Software Engineering - ICSE ’06 (pp. 12-29). ACM Publishing. https://doi.org/10.1145/1134285.1134288
  6. Bourque, P., & Fairley, R. E. (2014). Guide to the software engineering - Body of knowledge. IEEE Computer Society.
  7. Chemuturi, M. (2009). Software estimation best practices, tools & techniques: A complete guide for software project estimators. J. Ross Publishing.
  8. Fernandez-Diego, M., Mendez, E. R., Gonzalez-Ladron-De-Guevara, F., Abrahao, S., & Insfran, E. (2020). An update on effort estimation in agile software development: A systematic literature review. IEEE Access, 8, 166768-166800. https://doi.org/10.1109/ACCESS.2020.3021664
  9. Hair, J. F., Page, M., & Brunsveld, N. (2020). Essentials of business research methods (4th Ed.). Routledge.
  10. Hamouda, A. E. D. (2014). Using agile story points as an estimation technique in CMMI organizations. In 2014 Agile Conference (pp. 16-23). IEEE Publishing. https://doi.org/10.1109/AGILE.2014.11
  11. Jørgensen, M. (2016). Unit effects in software project effort estimation: Work-hours gives lower effort estimates than workdays. Journal of Systems and Software, 117, 274-281. https://doi.org/10.1016/j.jss.2016.03.048
  12. Khatri, S. K., Malhotra, S., & Johri, P. (2016). Use case point estimation technique in software development. In 2016 5th international conference on reliability, infocom technologies and optimization (trends and future directions)(ICRITO) (pp. 123-128). IEEE Publishing. https://doi.org/10.1109/ICRITO.2016.7784938
  13. Lenarduzzi, V., Lunesu, I., Matta, M., & Taibi, D. (2015). Functional size measures and effort estimation in agile development: A replicated study. In International Conference on Agile Software Development (pp. 105-116). Springer. https://doi.org/10.1007/978-3-319-18612-2_9
  14. López-Martínez, J., Ram´ırez-Noriega, A., Ju´arez-Ram´ırez, R., Licea, G., & Mart´ınez-Ram´ırez, Y. (2017). Analysis of Planning poker factors between university and enterprise. In 2017 5th International Conference in Software Engineering Research and Innovation (CONISOFT) (pp. 54-60). IEEE Publishing. https://doi.org/10.1109/CONISOFT.2017.00014
  15. López-Martínez, J., Ramírez-Noriega, A., Juárez-Ramírez, R., Licea, G., & Jiménez, S. (2018). User stories complexity estimation using Bayesian networks for inexperienced developers. Cluster Computing, 21(1), 715-728. https://doi.org/10.1007/s10586-017-0996-z
  16. Mahnič, V., & Hovelja, T. (2012). On using planning poker for estimating user stories. Journal of Systems and Software, 85(9), 2086-2095. https://doi.org/10.1016/j.jss.2012.04.005
  17. Malgonde, O., & Chari, K. (2019). An ensemble-based model for predicting agile software development effort. Empirical Software Engineering, 24, 1017-1055. https://doi.org/10.1007/s10664-018-9647-0
  18. Munialo, S. W., & Muketha, G. M. (2016). A review of agile software effort estimation methods. International Journal of Computer Applications Technology and Research, 5(9), 612-618.
  19. Pasuksmit, J., Thongtanunam, P., & Karunasekera, S. (2021). Towards just-enough documentation for agile effort estimation: What information should be documented? IN 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) (pp. 114-125). IEEE Publishing. https://doi.org/10.1109/ICSME52107.2021.00017
  20. Popli, R., & Chauhan, N. (2013). A sprint-point based estimation technique in Scrum. In 2013 International Conference on Information Systems and Computer Networks (pp. 98-103). IEEE Publishing. https://doi.org/10.1109/ICISCON.2013.6524182
  21. Popli, R., & Chauhan, N. (2014). Agile estimation using people and project related factors. In 2014 International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 564-569). IEEE Publishing. https://doi.org/10.1109/IndiaCom.2014.6828023
  22. Prakash, B., & Viswanathan, V. (2017). A survey on software estimation techniques in traditional and agile development models. Indonesian Journal of Electrical Engineering and Computer Science, 7(3), 867-876. https://doi.org/10.11591/ijeecs.v7.i3.pp867-876
  23. Project-Management.com. (2019). 10 key principles of agile software development. Project Management.Com. https://project-management.com/10-key-principles-of-agile-software-development/
  24. Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK® Guide) (Sixth Edit). Project Management Institute, Inc. https://www.pmi.org/
  25. Ramessur, M. A., & Nagowah, S. D. (2020). Factors affecting sprint effort estimation. In Advances in Intelligent Systems and Computing (Vol. 1089, pp. 507-518). Springer. https://doi.org/10.1007/978-981-15-1483-8_43
  26. Rosa, W., Clark, B. K., Madachy, R., & Boehm, B. (2021). Empirical effort and schedule estimation models for agile processes in the US DoD. In IEEE Transactions on Software Engineering (pp. 1-1). IEEE Publishing. https://doi.org/10.1109/TSE.2021.3080666
  27. Sommerville, I. (2011). Software engineering (9th Ed.). Addison-Wesley.
  28. Sudarmaningtyas, P., & Mohamed, R. (2021). A review article on software effort estimation in agile methodology. Pertanika Journal of Science and Technology, 29(2), 837-861. https://doi.org/10.47836/pjst.29.2.08
  29. Sudarmaningtyas, P., & Mohamed, R. B. (2020). Extended planning poker: A proposed model. In 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 179-184). IEEE Publishing. https://doi.org/10.1109/ICITACEE50144.2020.9239165
  30. Trendowicz, A., & Jeffery, R. (2014). Software project effort estimation: foundation and best practice guidelines for success. Springer International Publishing. https://doi.org/10.1007/978-3-319-03629-8
  31. Yuliansyah, H., Qudsiah, S. N., Zahrotun, L., & Arfiani, I. (2018). Implementation of use case point as software effort estimation in Scrum Framework. IOP Conference Series: Materials Science and Engineering, 403, Article 012085. https://doi.org/10.1088/1757-899X/403/1/012085
  32. Zhang, X., Hu, T., Dai, H., & Li, X. (2010). Software development methodologies, trends, and implications. Information Technology Journal, 9(8), 173-178.
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