Whether software metrics can still predict class error proneness in a system's post-release evolution is still a question to be answered.
A missed diagnosis on clinical grounds was classified as a class 1 error (if detected before death, this would probably have caused a therapeutic change with possible altered outcome) or as a class II error (if known before death, this diagnosis would not have led to a change in therapy).
This paper presents the results from an empirical study that investigated the relationship between the bad smells and class error probability in three error-severity levels in an industrial-strength open source system.
There has been no empirical evidence linking the bad smells with class error probability so far.
Many empirical studies have found that software metrics can predict class error proneness and the prediction can be used to accurately group error-prone classes.
The segmentation error time includes the amount of time that is assigned to the wrong class, missed class time, and false alarm class time: Class error time: The class error time is the amount of time that has been assigned to an incorrect class.
Table 4 Accuracy assessment of the LULC classes across the three study sites Site LULC class Error Omis.
These studies, however, focused on the relationship between software metrics and class error proneness during the development phase of software projects.
Although there is a plethora of empirical studies linking software metrics to errors and error proneness of classes in object-oriented systems, the link between the bad smells and class error probability in the evolution of object-oriented systems after the systems are released has not been explored.
This study examined three releases of the Eclipse project and found that although some metrics can still predict class error proneness in three error-severity categories, the accuracy of the prediction decreased from release to release.
