Medical software bug prediction based on static analysis

Monitoring and predicting the increasing or decreasing trend of bug number in a software is of great importance to both software developers and users. Accurate predicting of number of software bug will help developers make timely and correct decision. For software users, knowing the possible number of software bugs will enable them to take seasonable actions to cope with loss caused by possible software bugs. Medical software is vital to people's health, and therefore, the bug prediction of medical software is more important and essential than the ordinary software. To accomplish this goal, we present a method based on static analysis and correlation analysis to predict the bug number of medical image informatics software 3D Slicer and ITK. We obtain the complexity metrics through static analysis, then get the bug predicted value via correlation analysis between existing bug number and complexity metrics. The core idea of this paper is that the changes of software complexity metrics obtained by static analysis can reflect the changes of the bug number, and the predicted results prove the feasibility of this method. In addition, the method proposed in this paper is also applicable to other types of software other than medical software.