Software Effort Estimation for Improved Decision Making

Now a days, software development organizations use machine learning techniques in different areas to improve decision-making process so that their performance is boosted. In this dissertation, with the goal of increasing the accuracy in effort estimates, we applied programming models in an environment of software development organizations. We collected empirical data from two organizations and constructed a consolidated data sets. The programming models applied in this study are K-Means clustering, Support Vector Machines using polynomial kernel, Random Forest, Linear Regression, K Nearest Neighbor and Neural Networks using ORANGE tool. The obtained results demonstrate the use of data mining and machine learning techniques in general increases the accuracy of predictions with lesser error magnitude as compared to experts. Moreover, we recommend application of programming models in comparable environment of software development organizations to get reliable and more generalized predictions for decision making.