Assessing the Effectiveness of MoSCoW Prioritization in Software Development: A Holistic Analysis across Methodologies
DOI:
https://doi.org/10.4108/eetiot.6515Keywords:
MoSCoW, Mixed method approach, Sentiment Analysis, Requirement Prioritization, Software DevelopmentAbstract
Effective software Requirement Prioritization plays a pivotal role in the success of the Software Development process, ultimately contributing to the successful delivery of high-quality products. Among the various methods for Requirement Prioritization, the MoSCoW method has gained widespread adoption due to its ease of use. However, its overall effectiveness remains a subject of inquiry. This paper presents a rigorous assessment of the MoSCoW Requirement Prioritization technique, drawing insights from software developers who engage in the Prioritization process. Our evaluation encompasses a distinct perspective: that of the developers tasked with Prioritization. The feedback solicited from developers encapsulates a diverse set of criteria, shedding light on the method's efficacy. Additionally, we perform sentiment analysis on the user experience of the Prioritization task to corroborate the method's accuracy and efficiency. Our study unfolds through a practical exercise involving the Prioritization of a predefined set of requirements using MoSCoW principles. A mixed method approach is employed for the purpose of assessing the effectiveness of MoSCoW. The findings of our quantitative research underscore the method's limitations, indicating that it may not be as effective and precise as previously believed. Furthermore, through qualitative analysis, we are able to highlight the complexities and challenges associated with MoSCoW-based Prioritization. The insights gained from this analysis prompt contemplation regarding the potential introduction of an evolved Requirement Prioritization method, while leveraging MoSCoW as a foundational framework. This research aims to inform the ongoing evolution of Requirement Prioritization methodologies, ultimately enhancing the efficiency and accuracy of Software Development processes.
Downloads
References
[1] Licorish, S. A., Savarimuthu, B.T.R., & Keertipati, S. (2017, June). Attributes that predict which features to fix: Lessons for app store mining. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (pp. 108-117).
[2] Ali, A., Hafeezb , Y., Hussain, S., & Yang, S. (2020). Role of requirement prioritization technique to improve the quality of highly-configurable systems. IEEE Access, 8, 27549-27573.
[3] Kravchenko, T., Bogdanova, T., & Shevgunov, T. (2022, April). Ranking requirements using MoSCoW methodology in practice. In Computer Science On-line Conference (pp. 188-199). Cham: Springer International Publishing.
[4] Migiro, S. O., & Magangi, B. A. (2011). Mixed methods: A review of literature and the future of the new research paradigm. African journal of business management, 5(10), 3757-3764.
[5] Voola, P. &. B. A. V. "Comparison of requirements Prioritization techniques employing different scales of measurement.," ACM SIGSOFT Software Engineering Notes, vol. 38, no. 4 doi.org/10.1145/2492248.2492278, pp. 1-10, 2013.
[6] Ali Khan, J., Qasim, I., Khan, S. P., & Khan, Y. H. (2016). An Evaluation of Requirement Prioritization Techniques with ANP. International Journal of Advanced Computer Science and Applications, 7(7).
[7] Hatton, S. (2008, March). Choosing the right Prioritization method. In 19th Australian conference on software engineering (ASWEC 2008) (pp. 517-526). IEEE.
[8] Marthasari, G., Suharso, W., & Ardiansyah, F. A. (2018). Personal Extreme Programming with MoSCoW Prioritization for Developing Library Information System. Proceeding of the Electrical Engineering Computer Science and Informatics, 5(1), 537-541.
[9] Babar, M. I., Ghazali, M., Jawawi, D. N., Shamsuddin, S. M., & Ibrahim, N. (2015). PHandler: an expert system for a scalable software requirements Prioritization process. Knowledge-Based Systems, 84, 179-202.
[10] Achimugu, P., Selamat, A., & Ibrahim, R. (2016). ReproTizer: A fully implemented software requirements Prioritization tool. In Transactions on computational collective intelligence XXII (pp. 80-105). Springer Berlin Heidelberg.
[11] Jahan, M. S., Azam, F., Anwar, M. W., Amjad, A., & Ayub, K. (2019, October). A Novel Approach for Software Requirement Prioritization. In 2019 7th International Conference in Software Engineering Research and Innovation (CONISOFT) (pp. 1-7). IEEE.
[12] Ahmad, K. S., Ahmad, N., Tahir, H., & Khan, S. (2017, July). Fuzzy_MoSCoW: A fuzzy based MoSCoW method for the Prioritization of software requirements. In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies
[13] Hudaib, A., Masadeh, R., Qasem, M. H., & Alzaqebah, A. (2018). Requirements prioritization techniques comparison. Modern Applied Science, 12(2), 62.
[14] Almalki, S. (2016). Integrating Quantitative and Qualitative Data in Mixed Methods Research--Challenges and Benefits. Journal of education and learning, 5(3), 288-296.
[15] Vestola, M. (2010). A comparison of nine basic techniques for requirements prioritization. Helsinki University of Technology, 1-8.
[16] Pentang, J. T., & Pentang, J. (2021). Quantitative data analysis. Holy Angel University Graduate School of Education: Research and academic writing. http://dx. doi. org/10.13140/RG, 2(23906.45764), 1.
[17] Lester, J. N., Cho, Y., & Lochmiller, C. R. (2020). Learning to do qualitative data analysis: A starting point. Human resource development review, 19(1), 94-106.
[18] Lochmiller, C. R. (2021). Conducting thematic analysis with qualitative data. The Qualitative Report, 26(6), 2029-2044.
[19] Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & health sciences, 15(3), 398-405.
[20] Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. NursingPlus open, 2, 8-14.
[21] Heimerl, F., Lohmann, S., Lange, S., & Ertl, T. (2014, January). Word cloud explorer: Text analytics based on word clouds. In 2014 47th Hawaii international conference on system sciences (pp. 1833-1842). IEEE.
[22] Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338.
[23] Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2, 325-347.
[24] Elbagir, S., & Yang, J. (2019, March). Twitter sentiment analysis using natural language toolkit and VADER sentiment. In Proceedings of the international multiconference of engineers and computer scientists (Vol. 122, No. 16). sn.
[25] Chaudhri, A. A., Saranya, S. S., & Dubey, S. (2021). Implementation paper on analyzing COVID-19 vaccines on twitter dataset using tweepy and text blob. Annals of the Romanian Society for Cell Biology, 8393-8396.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
