Investigation of various data analysis techniques to identify change prone parts of an open source software

Abstract

Identifying and examining the change-prone parts of the software is gaining wide importance in the field of software engineering. This would help software practitioners to cautiously assign the resources for testing and maintenance. Software metrics can be used for constructing various classification models which allow timely identification of change prone classes. There have been various machine learning classification models proposed in the literature. However, due to varying results across studies, more research needs to be done to increase the confidence in the results and provide a valuable conclusion. In this paper, we have used a number of data analysis techniques (14 machine learning techniques and a statistical technique) to construct change prediction models and performed statistical testing to compare the performance of these models. The application of a large number of techniques will allow for fair evaluation and will thus, increase the conclusion validity of the study. The results are validated on five releases of an open source, widely used operating system in mobile phone and tablet computers, ‘Android’. To make the results more generalizable, we have also conducted inter-release and cross-project predictions. The results conclude that the machine learning techniques are effective in predicting change prone classes and thus, should be widely used by researchers and practitioners to reduce maintenance effort and thus efficient and better development of software.