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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.
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.
We use LightNet to simultaneously predict class and orientation labels from complete and partial shapes.
The representations are designed to capture the nature of 3D surfaces in terms of their local geometry and predict class labels associated with such local geometries.
Whether software metrics can still predict class error proneness in a system's post-release evolution is still a question to be answered.
All 25 softmax outputs were then averaged to predict class.
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Existing explanations predict class-based engagement strategies.
Then, classifiers based on features above were used in combination to predict classes of test images.
Sometimes, a single classification rule is not powerful enough to sufficiently predict classes of new data.
It is shown in the results that the method was able to predict class-1 with 100% accuracy but failed to predict class-2.
The same method is then used to predict classes for new samples (validation set).
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