A large system often goes through multiple software project development cycles, in part due to changes in operation and development environments. For example, rapid turnover of the development team between releases can influence software quality, making it important to mine software project data over multiple system releases when building defect predictors. Data collection of software attributes are often conducted independent of the quality improvement goals, leading to the availability of a large number of attributes for analysis. Given the problems associated with variations in development process, data collection, and quality goals from one release to another emphasizes the importance of selecting a best-set of software attributes for software quality prediction. Moreover, it is intuitive to remove attributes that do not add to, or have an adverse effect on, the knowledge of the consequent model. Based on real-world software projects’ data, we present a large case study that compares wrapper-based feature ranking techniques (WRT) and our proposed hybrid feature selection (HFS) technique. The comparison is done using both threefold cross-validation (CV) and three-fold cross-validation with risk impact (CVR). It is shown that HFS is better than WRT, while CV is superior to CVR.


Artificial Intelligence and Robotics | Databases and Information Systems | Other Computer Sciences