Statistical Analysis with Prediction Models of User Satisfaction in Software Project Factors

Abstract

We analyzed a volume of software project data and found significant user satisfaction in several software project factors. statistical significance A analysis (logistic regression) a collinearity analysis and determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. Eight prediction models were used to test the prediction potential of these factors: Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were significant in predicting user satisfaction: client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. They provided 82.71% prediction accuracy when used with a neural network prediction model. These findings may directly benefit software development managers.