Machine Learning Approach Integrated with MoSCoW Method for Parallel Requirements Priorization

Kawthar Ishag  Ali Fadlallah; Moawia Elfaki Yahia  Eldow; Anwer Mustafa  Hilal; Khalid Mohammed Osman  Saeed; Mohammed Mohammed Osman  Mokhtar

Authors

Kawthar Ishag Ali Fadlallah Omdurman Islamic University
Moawia Elfaki Yahia Eldow Faculty of Mathematical Science, University of Khartoum, Khartoum 11115, Sudan, University of North Texas, Denton, TX, USA
Anwer Mustafa Hilal Department of Information System, Omdurman Islamic University, Omdurman, Sudan
Khalid Mohammed Osman Saeed Department of Information System, Omdurman Islamic University, Omdurman, Sudan
Mohammed Mohammed Osman Mokhtar Department of Information System, Omdurman Islamic University, Omdurman, Sudan

Abstract

Requirements prioritization (RP) is one of the vital activities carried out through requirements engineering process. Requirements prioritization includes the selection of requirements that are reflected more important from elicited list of stakeholders' requirements. Making an incorrect selection will not only reduction the quality of the developed software but it will also earn extra cost for refinement processes in later stages. Thus, requirements prioritization would aid to determine the most appropriate requirements in different software product releases. Many research focusing on prioritizing the requirements using one or several criteria like time, dependency, and scalability. However, most of these studies address sequential prioritization only. To the best of our knowledge, no research has explored parallel ranking in prioritization, which allows for simultaneous requirements implementation, thereby reducing implementation time. Furthermore, as the volume of requirements grows, scalability becomes a critical issue. Manual prioritization is time-consuming and increases the likelihood of overlooking essential. Machine learning is increasingly popular for automating requirements prioritization. In this study we developed automated parallel requirements prioritization approach (APRP) for determine the requirements priority level in parallel format using Random Forest classifier based MoSCoW method (RF-MM). The proposed approach consists of two main modules, data elicitation and pre-processing module and prioritization module, which include established weight assignment, MoSCoW parallel prioritization, and classifier methods. Experiments on the industrial dataset (Testcase MIS system with priority) revealed that the total implementation time for sequential ranking was 76.0 seconds, whereas it was reduced to 33.0 seconds for parallel ranking. Thus, parallel ranking reduced implementation time by more than half. We achieved a maximum accuracy of 94.87%, precision of 92.31%, and recall of 92.31%.