Open AccessA Predictive Risk Model for Software Projects’ Requirement Gathering Phase
Author(s) -
Beatrice O. Akumba,
Samera U. Otor,
Agaji Iorshase,
Barnabas T. Akumba
Publication year - 2020
Publication title -
international journal of innovative science and research technology
Language(s) - English
Resource type - Journals
ISSN - 2456-2165
DOI - 10.38124/ijisrt20jun066
Subject(s) - software , computer science , confusion matrix , naive bayes classifier , predictive modelling , classifier (uml) , software quality , reliability engineering , risk analysis (engineering) , software development , machine learning , data mining , artificial intelligence , engineering , support vector machine , medicine , programming language
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%.