A Predictive Risk Model for Software Projects’ Requirement Gathering Phase

Authors : Beatrice O. Akumba; Samera U. Otor; Iorshase Agaji; Barnabas T. Akumba

Volume/Issue : Volume 5 - 2020, Issue 6 - June

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3eva5Ac

DOI : 10.38124/IJISRT20JUN066

The initial stage of the software development lifecycle is the requirement gathering and analysis phase. Predicting risk at this phase is very crucial because cost and efforts can be saved while improving the quality and efficiency of the software to be developed. The datasets for software requirements risk prediction have been adopted in this paper to predict the risk levels across the software projects and to ascertain the attributes that contribute to the recognized risk in the software projects. A supervised machine learning technique was used to predict the risk across the projects using Naïve Bayes Classifier technique. The model was able to predict the risks across the projects and the performance metrics of the risk attributes were evaluated. The model predicted four (4) as Catastrophic, eleven (11) as High, eighteen (18) as Moderate, thirty-three (33) as Low and seven (7) as insignificant. The overall confusion matrix statistics on the risk levels prediction by the model had accuracy to be 98% with confidence interval (CI) of 95% and Kappa 97%.

Keywords : Naïve Bayes Classifier, SDLC, Risk Prediction, Software Projects, Risk Outcomes, Risk Levels


Paper Submission Last Date
30 - June - 2023

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.