# Transfer Convolutional Neural Network for Cross-Project Defect Prediction
## 5 Results  
Table 5 shows the average performance of each examined CPDP model with a given target project, calculated by the following procedure: (1) Given the target project, we separately evaluated the performance of a CPDP model trained by one source project. Through applying the nine source projects, we could obtain nine prediction models for a given target project. (2) We calculated the average performance of nine models and filled them into the table. The next-to-last row shows the win/tie/loss counts of TCNN versus other approaches.  
Figure 4 presents the corresponding Scott-Knott ESD test results (including 90 pairs of CPDP tasks formed by 10 datasets). By comprehensive observation, TCNN outperformed the nine reference methods in our experiments. Following are the answers for two research questions.  
Answer for RQ1: Can TCNN Perform Better than the Classic CPDP Methods that Use Only Traditional Handcrafted Features?  
In Table 5, the next-to-last row shows that TCNN can win most performance comparisons in 90 combinations of CPDP tasks. The average F-Measure of TCNN was 0.532. Compared to five classic CPDP methods that use only traditional handcrafted features, TCNN outperformed DG, TCA, NNFilter, LR, and TCA+ by 6.2%, 7.2%, 9.5%, 9.6%, and 15.1%, respectively.  
The following two points can be observed for this research question:  
(1) TCNN, which considers concatenating the DL-generated features with the handcrafted features, could perform better than the CPDP methods that use only traditional handcrafted features.  
(2) DG could achieve better performance in terms of average F-measure by comparing with other conventional CPDP methods. However, as shown in Figure 4, the DG model had a lower performance limit in 90 pairs of CPDP tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Target Project** & **LR** & **NNFilter** & **DG** & **TCA** & **TCA+** & **DBN** & **CNN** & **DPDBN** & **DPCNN** & **TCNN** \\ \hline Camel & 0.314 & 0.324 & 0.336 & 0.336 & **0.337** & 0.315 & 0.322 & 0.326 & 0.333 & 0.331 \\ Forrest & 0.183 & 0.181 & 0.143 & 0.129 & 0.127 & 0.129 & 0.145 & 0.139 & 0.185 & **0.216** \\ Ivy & 0.272 & 0.259 & 0.256 & 0.266 & 0.246 & 0.227 & 0.255 & 0.257 & 0.257 & **0.303** \\ Log4j & 0.631 & 0.643 & 0.639 & 0.681 & 0.676 & 0.658 & 0.647 & 0.687 & 0.663 & **0.702** \\ Lucene & 0.585 & 0.595 & 0.640 & 0.625 & 0.539 & 0.585 & 0.604 & 0.624 & 0.622 & **0.658** \\ Poi & 0.633 & 0.625 & 0.674 & 0.605 & 0.598 & 0.600 & 0.624 & 0.651 & 0.650 & **0.686** \\ Synapse & 0.507 & 0.507 & **0.533** & 0.528 & 0.542 & 0.449 & 0.490 & 0.510 & 0.510 & 0.528 \\ Velocity & 0.498 & 0.486 & 0.512 & 0.501 & 0.304 & 0.447 & 0.477 & 0.480 & 0.501 & **0.522** \\ Xalan & 0.599 &  
Answer for RQ2: How Effective Is the TCNN Approach Compared to State-of-the-Art SDP Methods Adopting DL-Learning Features?  
As Table 5 shows, our approach produced the best results in most performance comparisons. Even though the performance of TCNN was not always best, the average F-measure of TCNN was 0.532, which outperformed DPCNN, CNN, DPDBN, and DBN by 6.5%, 6.7%, 10.1%, and 14.2%, respectively.  
The following two points can be observed for this research question:  
(1) Overall, the methods using CNN-generated features (TCNN, DPCNN, CNN) would yield better predictive performance than DBN-generated features (DPDBN, DBN) in our 90 pairs of CPDP tasks.  
(2) TCNN performed better than the SDP methods adopting CNN-generated or DBN-generated features directly.  
_In summary, the experimental results showed that the proposed TCNN method tended to produce better CPDP performance than the other nine methods we examined. We believe that the performance of CPDP can generally be improved by using the TCNN-generated transferable joint features. This improvement will more effectively help the software quality assurance team find defect-prone modules or files, so the team could allocate resources more efficiently._