Abstract:
The purpose of this study is to determine a type of software metric at file level exhibiting
the best prediction performance. Studies have shown that software process metrics are better
predictors of software faults than software product metrics. However, there is need for a
specific software process metric which can guarantee the best fault prediction performances
consistently across different experimental contexts. We collected software metrics data from
Open Source Software projects. We used logistic regression and linear regression algorithms
to predict bug status and number of bugs corresponding to a file, respectively. The prediction
performance of these models was evaluated against numerical and graphical prediction
model performance measures. We found that change burst metrics exhibit the best numerical
performance measures and have the highest fault detection probability and least cost of
misclassification of software components.