Abstract
Changes in source code of the software products are inevitable. We need to change the source code to fix the feature improvements, new features and bugs. Feature improvements, new features and bugs are collectively termed as issues. The changes in the source code of the software negatively impact its product, but necessary for the evolution of the software. The changes in source code are quantified using entropy based measure and it is called the complexity of code changes. In this paper, we built regression models to predict the next release time of software using the complexity of code changes (entropy), feature improvements, new feature implementation and bugs fixed. The regression models have been built using Multiple Linear Regression (MLR), various kernel functions based Support Vector Regression (SVR) and k-Nearest Neighbor (k-NN) methods. The proposed models have been empirically validated using four open source sub-projects of the Apache software foundation. The proposed models exhibit a good fit. The developed models will assist release managers in release planning of the software.
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