Prediction of the Complexity of Code Changes Based on Number of Open bugs, New Feature and Feature Improvement

Abstract

During the last decade, a paradigm shift has been taken place in the software development process. Advancement in the internet technology has eased the software development under distributed environment irrespective of geographical locations. Result of this, Open Source Software systems which serve as key components of critical infrastructures in the society are still ever-expanding now. Open source software is evolved through an active participation of the users in terms of reporting of bugs, request for new features and feature improvements. These active users distributed across different geographical locations and are working towards the evolution of open source software. The code-changes due to bug fixes, new features and feature improvements for a given time period are used to predict the possible code changes in the software over a long run (potential complexity of code changes). It is evident that the open source software are evolved through these modification but an empirical understanding among the bug fix, new features, feature improvements and modifications in the files are unexplored till now. In this paper, we have predicted the potential of bugs that can be detected/fixed and new features, improvements that can be diffused in the software over a period of time. We have quantified the complexity of code changes (entropy) and after that predicted the complexity of code changes by applying Cobb-Douglas and extended Cobb-Douglas (two dimensions and three dimensions) based diffusion models. The developed models can be used to determine the quantitative value of complexity of code changes for reported bugs, new features and feature improvements in addition to their potential values. This empirical study mathematically models the interaction of a system (the debugging and code change system) with the external open world which will assist support managers in software maintenance activities and software evolution.