  Software defect prediction is an essential task during the software
development Lifecycle as it can help managers to identify the most
defect-proneness modules. Thus, it can reduce the test cost and assign testing
resources efficiently. Many classification methods can be used to determine if
the software is defective or not. Support Vector Machine (SVM) has not been
used extensively for such problems because of its instability when applied on
different datasets and parameter settings. The main parameter that influences
the accuracy is the choice of the kernel function. The use of kernel functions
has not been studied thoroughly in previous papers. Therefore, this research
examines the performance and accuracy of SVM with six different kernel
functions. Various public datasets from the PROMISE project empirically
validate our hypothesis. The results demonstrate that no kernel function can
give stable performance across different experimental settings. In addition,
the use of PCA as a feature reduction algorithm shows slight accuracy
improvement over some datasets.
