Khisaluddin Shaikh, Nabilah Filzah Mohd Radzuan, Hafiz Ahmed Ali
Abstract
Effort estimation remains a critical and unsolved challenge in global software development (GSD), where increase in project complexity, rapid technological growth, and the occurrence of distributed teams across the globe have declared traditional estimation paradigms insufficient for estimating a product. This updated systematic literature review thoroughly examines the evolution and empirical foundation of estimation methods in GSD, drawing on studies published between 2020 to 2024. Our analysis exposes a persistent trust in expert judgment and algorithmic models, with only experimental adaptation of AI/ML and hybrid approaches, none of which comprehensively address GSD-specific drivers such as time zone distribution, cultural differences, and coordination interruptions. Despite the proliferation of new techniques, there remains a noticeable scarcity of large-scale context-aware metric development, empirical validation and standardization. Particularly, our research gap mapping reveals that no prevailing method fully meets the core needs of modern GSD, indicating systemic weaknesses in methodological and practical applicability. By critically synthesizing these limitations and highlighting the disengagement between academic innovation and industrial adoption, this review provides not only an authoritative assessment of the current state-of-the-art methods for estimation but also a persuasive direction for the development of empirically grounded, standardized, and contextually adaptive estimation models. These insights are intended to guide researchers, practitioners, and policymakers towards resolving the long-standing bottlenecks that delay reliable effort estimation in global software engineering.
Keywords
Effort Estimation, Global Software Development, Systematic Literature Review.
Conclusion
Global software development has changed the dynamics of software development. It gave opportunities as well as challenges. This updated SLR provides a timely, critical synthesis of effort estimation practices, methods, and challenges in global software development, defining developments and current gaps in literature since 2020. By systematically mapping of traditional, algorithmic, and AI-driven estimation methods and contextualizing them within the complications of modern GSD, our review highlights the vital need for empirically validated, context-aware models and standardized metrics. The analysis underscores that no single approach or metric yet addresses the complicated, dynamic nature of GSD, especially in the face of emerging trends like remote-collaboration and increased project complexity. Our contributions advise both researchers and practitioners, providing a basis for methodological improvement and strategic adoption of new estimation approaches and frameworks. Future work must emphasize large-scale empirical validation, cross-domain dataset sharing, and the integration of human and contextual factors for estimation in GSD. Only through such joint, evidence-based efforts can determined the challenges of effort estimation in global software development be meaningfully addressed. We just want to admit that this research is an updated version of a SLR published 2014 by [3].
References
[1] P. Suresh Kumar, H. S. Behera, J. Nayak, and B. Naik, “A pragmatic ensemble learning approach for effective software effort estimation,” Innov Syst Softw Eng, vol. 18, no. 2, pp. 283–299, Jun. 2022, doi: 10.1007/s11334-020-00379-y.
[2] S. Hameed, Y. Elsheikh, and M. Azzeh, “An optimized case-based software project effort estimation using genetic algorithm,” Inf Softw Technol, vol. 153, Jan. 2023, doi: 10.1016/j.infsof.2022.107088.
[3] R. Britto, V. Freitas, E. Mendes, and M. Usman, “Effort estimation in global software development: A systematic literature review,” in Proceedings – 2014 IEEE 9th International Conference on Global Software Engineering, ICGSE 2014, Institute of Electrical and Electronics Engineers Inc., Oct. 2014, pp. 135–144. doi: 10.1109/ICGSE.2014.11.
[4] N. Azura Zakaria, A. Ritahani Ismail, A. Yakath Ali, N. Hidayah Mohd Khalid, and N. Zainal Abidin, “Software Project Estimation with Machine Learning,” 2021. [Online]. Available: http://promise.site.uottawa.ca/serepository/datasets-page.html
[5] A. Jadhav, M. Kaur, and F. Akter, “Evolution of Software Development Effort and Cost Estimation Techniques: Five Decades Study Using Automated Text Mining Approach,” 2022, Hindawi Limited. doi: 10.1155/2022/5782587.
[6] M. A. Latif, M. K. Khan, and U. Hani, “Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction,” KSII Transactions on Internet and Information Systems, vol. 17, no. 5, pp. 1356–1376, May 2023, doi: 10.3837/tiis.2023.05.003.
[7] D. Wickramaarachchi and R. Lai, “Effort estimation in global software development – a systematic review,” Computer Science and Information Systems, vol. 14, no. 2, pp. 393–421, Jun. 2017, doi: 10.2298/CSIS160229007W.
[8] B. Hayat Malik, S. Faroom, M. Nauman Ali, N. Shehzad, S. Yousaf, and H. Saleem, “Geographical Distance and Communication Challenges in Global Software Development: A Review,” 2018. [Online]. Available: www.ijacsa.thesai.org
[9] H. and G. E. and S. K. Siakas Errikos and Rahanu, “Towards Reducing Communication Gaps in Multicultural and Global Requirements Elicitation,” in Systems, Software and Services Process Improvement, P. and M. R. and R. M. Yilmaz Murat and Clarke, Ed., Cham: Springer International Publishing, 2021, pp. 257–277.
[10] E. Y. Cho, “Exploring Challenges in Global Software Development A Case Study of a Collaboration between a Korean and a Swedish Company.”
[11] J. L. Scott Charles P. R. and Wildman, “Culture, Communication, and Conflict: A Review of the Global Virtual Team Literature,” in Leading Global Teams: Translating Multidisciplinary Science to Practice, R. L. Wildman Jessica L. and Griffith, Ed., New York, NY: Springer New York, 2015, pp. 13–32. doi: 10.1007/978-1-4939-2050-1_2.
[12] A. S. Ghiduk and A. M. Qahtani, “An empirical study of local-decision-making-based software customization in distributed development,” IET Software, vol. 15, no. 2, pp. 174–187, Apr. 2021, doi: 10.1049/sfw2.12016.
[13] M. Shameem, M. Nadeem, and A. T. Zamani, “Genetic algorithm based probabilistic model for agile project success in global software development,” Appl Soft Comput, vol. 135, Mar. 2023, doi: 10.1016/j.asoc.2023.109998.
[14] M. Ahmed et al., “A Hybrid Model for Improving Software Cost Estimation in Global Software Development,” Computers, Materials and Continua, vol. 78, no. 1, pp. 1399–1422, 2024, doi: 10.32604/cmc.2023.046648.
[15] Mark. Petticrew and Helen. Roberts, Systematic reviews in the social sciences : a practical guide. Blackwell Pub., 2006.
[16] E. Dantas, M. Perkusich, E. Dilorenzo, D. F. S. Santos, H. Almeida, and A. Perkusich, “Effort estimation in agile software development: An updated review,” in Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, Knowledge Systems Institute Graduate School, 2018, pp. 496–501. doi: 10.18293/SEKE2018-003.
[17] S. Jalali and C. Wohlin, “Global software engineering and agile practices: A systematic review,” Oct. 2012. doi: 10.1002/smr.561.
[18] E. Hossain, M. Ali Babar, and H. Y. Paik, “Using scrum in global software development: A systematic literature review,” in Proceedings – 2009 4th IEEE International Conference on Global Software Engineering, ICGSE 2009, 2009, pp. 175–184. doi: 10.1109/ICGSE.2009.25.
[19] J. Kroll, R. Prikladnicki, J. Luis, and N. Audy, “Mapping the Evolution of Research on Global Software Engineering-A Systematic Literature Review. MAPPING THE EVOLUTION OF RESEARCH ON GLOBAL SOFTWARE ENGINEERING A Systematic Literature Review,” 2011. [Online]. Available: https://www.researchgate.net/publication/220708951
[20] F. Q. B. Da Silva, C. Costa, A. C. C. França, and R. Prikladinicki, “Challenges and solutions in Distributed Software Development Project Management: A systematic literature review,” in Proceedings – 5th International Conference on Global Software Engineering, ICGSE 2010, IEEE Computer Society, 2010, pp. 87–96. doi: 10.1109/ICGSE.2010.18.
[21] F. Q. B. Da Silva, R. Prikladnicki, A. C. C. França, C. V. F. Monteiro, C. Costa, and R. Rocha, “An evidence-based model of distributed software development project management: Results from a systematic mapping study,” Journal of software: Evolution and Process, vol. 24, no. 6, pp. 625–642, Oct. 2012, doi: 10.1002/smr.563.
[22] D. Mishra, A. Mishra, R. Colomo-Palacios, and C. Casado-Lumbreras, “Global Software Development and Quality Management: A Systematic Review,” 2013.
[23] S. Schneider, R. Torkar, and T. Gorschek, “Solutions in global software engineering: A systematic literature review,” Int J Inf Manage, vol. 33, no. 1, pp. 119–132, 2013, doi: 10.1016/j.ijinfomgt.2012.06.002.
[24] R. Prikladnicki and J. L. N. Audy, “Process models in the practice of distributed software development: A systematic review of the literature,” 2010, Elsevier B.V. doi: 10.1016/j.infsof.2010.03.009.
[25] M. J. Monasor, A. Vizcaíno, M. Piattini, and I. Caballero, “Preparing students and engineers for global software development: A systematic review,” in Proceedings – 5th International Conference on Global Software Engineering, ICGSE 2010, IEEE Computer Society, 2010, pp. 177–186. doi: 10.1109/ICGSE.2010.28.
[26] E. H. M. H. TaniaF. C. T. Euclides Alfredo Matusse, Metrics and Indicators to Assist in the Distribution of Process Steps for Distributed Software Development: A Systematic Review. IEEE, 2012.
[27] D. Šmite, C. Wohlin, T. Gorschek, and R. Feldt, “Empirical evidence in global software engineering: A systematic review,” Empir Softw Eng, vol. 15, no. 1, pp. 91–118, Feb. 2010, doi: 10.1007/s10664-009-9123-y.
[28] I. Nurdiani, R. Jabangwe, D. Šmite, and D. Damian, “Risk identification and risk mitigation instruments for global software development: Systematic review and survey results,” in Proceedings – 2011 6th IEEE International Conference on Global Software Engineering Workshops, ICGSE Workshops 2011, 2011, pp. 36–41. doi: 10.1109/ICGSE-W.2011.16.
[29] S. Nidhra, M. Yanamadala, W. Afzal, and R. Torkar, “Knowledge transfer challenges and mitigation strategies in global software development—A systematic literature review and industrial validation,” Int J Inf Manage, vol. 33, no. 2, pp. 333–355, Apr. 2013, doi: 10.1016/j.ijinfomgt.2012.11.004.
[30] I. Steinmacher, A. P. Chaves, and M. A. Gerosa, “Awareness support in distributed software development: A systematic review and mapping of the literature,” Apr. 01, 2013, Kluwer Academic Publishers. doi: 10.1007/s10606-012-9164-4.
[31] V. Gomes and S. Marczak, “Problems? We all know we have them. Do we have solutions too? A literature review on problems and their solutions in global software development,” in Proceedings – 2012 IEEE 7th International Conference on Global Software Engineering, ICGSE 2012, 2012, pp. 154–158. doi: 10.1109/ICGSE.2012.43.
[32] Nguyen Duc Anh, Daniela S. Cruzes, and Reidar Conradi, Dispersion, Coordination and Performance in Global Software Team A Systematic Review. ACM Digital Library, 2013.
[33] I. Steinmacher, A. P. Chaves, and M. A. Gerosa, “Awareness Support in Global Software Development: A Systematic Review Based on the 3C Collaboration Model.”
[34] B. Kitchenham, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” 2007. [Online]. Available: https://www.researchgate.net/publication/302924724
[35] H. Zhang and M. A. Babar, “On searching relevant studies in software engineering On searching relevant studies in software engineering On Searching Relevant Studies in Software Engineering,” 2010. [Online]. Available: https://hdl.handle.net/10344/730
[36] E. Mendes, V. T. Vaz, and F. Muradas, “An expert-based requirements effort estimation model using bayesian networks,” in Lecture Notes in Business Information Processing, Springer Verlag, 2016, pp. 79–93. doi: 10.1007/978-3-319-27033-3_6.
[37] R. Britto, E. Mendes, and J. Borstler, “An Empirical Investigation on Effort Estimation in Agile Global Software Development,” in Proceedings – 2015 IEEE 10th International Conference on Global Software Engineering, ICGSE 2015, Institute of Electrical and Electronics Engineers Inc., Aug. 2015, pp. 38–45. doi: 10.1109/ICGSE.2015.10.
[38] M. El Bajta, “Analogy-based software development effort estimation in global software development,” in Proceedings – 2015 IEEE 10th International Conference on Global Software Engineering Workshops, ICGSEW 2015, Institute of Electrical and Electronics Engineers Inc., Aug. 2015, pp. 51–54. doi: 10.1109/ICGSEW.2015.19.
[39] R. Britto, E. Mendes, and C. Wohlin, “A specialized global software engineering taxonomy for effort estimation,” in Proceedings – 11th IEEE International Conference on Global Software Engineering, ICGSE 2016, Institute of Electrical and Electronics Engineers Inc., Sep. 2016, pp. 154–163. doi: 10.1109/ICGSE.2016.11.
[40] Mr.S.Ramacharan and Dr.K. Venu Gopala Rao, Scheduling Based Cost Estimation Model: An Effective Empirical Approach for GSD Project2016 Thirteenth International Conference on Wireless and Optical Communications Networks (WOCN) : 21-23 July. IEEE, 2016.
[41] B. W. Boehm, “Software Engineering Economics.”
[42] E. Mendes, “Cost Estimation Techniques for Web Projects,” 2007. [Online]. Available: https://api.semanticscholar.org/CorpusID:107768033
[43] M. Cohn, “Agile estimating and planning”.
[44] N. Panlilio-Yap, “Software Estimation Using the SLIM Too l.”
[45] D. Šmite, C. Wohlin, Z. Galviņa, and R. Prikladnicki, “An Empirically Based Terminology and Taxonomy for Global Software Engineering.”
How to Cite This Paper
Khisaluddin Shaikh, Nabilah Filzah Mohd Radzuan, Hafiz Ahmed Ali (2025). Effort Estimation in Global Software Development: An Updated Systematic Literature Review. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
