Fault-Proneness of Open Source Systems: An Empirical Analysis

Developing quality software is a very complex job considering the complexity and size of software developed these days. Early prediction of software quality assists in optimizing testing resources. Many fault prediction models have been developed using several internal attributes and different machine learning techniques. However, the open-source community still lacks a concise knowledge about what types of internal attributes affect the software quality the most. In this work, an empirical investigation is conducted to explore the relationships between internal attributes of open-source systems and their fault-proneness. The results of the empirical analysis showed that by selecting only nine internal attributes, the fault prediction models accuracy did not decrease significantly. This indicates that only a subset of these internal attributes is worth collection and investigation. By focusing on a small set of internal attributes, the quality assurance team can save time and resources while achieving high accuracy fault- proneness predictions.