Predicting Defect-Prone Software Modules at Different Logical Levels

Effective software defect estimation can bring cost reduction and efficient resources allocation in software development and testing. Usually, estimation of defect-prone modules is based on the supervised learning of the modules at the same logical level. Various practical issues may limit the availability or quality of the attribute-value vectors extracting from the high-level modules by software metrics. In this paper, the problem of estimating the defect in high-level software modules is investigated with a multi-instance learning (MIL) perspective. In detail, each high-level module is regarded as a bag of its low-level components, and the learning task is to estimate the defect-proneness of the bags. Several typical supervised learning and MIL algorithms are evaluated on a mission critical project from NASA. Compared to the selected supervised schemas, the MIL methods improve the performance of the software defect estimation models.