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
Abstract :
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
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
