## 8 Conclusions and Future Work  
In this study, three different software projects with more than 472,000 lines of code were developed by a seven-person development team by adhering largely to the life cycle and the work environment of XP and by practicing TDD. A project management tool was also developed to help manage the projects effectively by keeping track of the development progress and status of the observed faults. Then, a data set named SFP XP-TDD was created using the software metrics data of these software in order to conduct software with 547,546 fault prediction research, which was then used in the study for data sets with large record numbers.  
The experimental results of the study were also used to benchmark against data sets from Apache Active MQ and Eclipse. Machine learning models were trained and tested using the data set to perform software fault prediction. Experiments were performed on the data set with five different base classifiers and the RF ensembles of these classifiers. The experimental results demonstrate that higher accuracy rates were achieved with the ensemble RFs when compared to their respective base classifiers. Increases were observed not only in ACC values, but also in KE and AUC values. Considering the general averages, the value of ACC was observed to be increased by 2.12%. Furthermore, a rise in the averages of AUC and KE values support the performance increase in classifiers. In addition to the average performance values, an increase was observed in all three metrics with RF ensembles of 10 of 15 algorithms seen in Tables 3-5.  
This study used OO metrics, entropy metrics, and change metrics, in addition to CK metrics on the data sets to conduct experiments and test the performance rates of algorithms. Besides utilizing ML algorithm-based software fault prediction, the current study also used several other software fault predictions alongside DL algorithms that achieved significant success in many diverse fields, including computer vision, natural language processing, and speech recognition. Finally, to conduct software fault prediction with deep learning methods, this study made a software fault prediction using a DL neural network called RNNBDL, comprised of LSTM and BILSTM algorithms, in addition to a combination of CNN, LSTM, and BiLSTM.  
In the study carried out for data sets with large record numbers, it was also concluded that successful outcomes were achieved when an RNN-based deep-learning model with many layers was utilized in fault prediction. The n-way ANOVA statistical method compared both the results of machine learning and deep learning algorithms. Accordingly, the test results concluded that it is possible to prefer using deep learning algorithms instead of machine learning algorithms in studies performed with a 95% confidence rate for large software fault data sets. It is viable to employ the resulting data set in cross-project defect prediction. The subsequently designed studies will focus on generating a new data set using Java Doc documents, aiming to incorporate transfer learning into the novel work developed with a brand new hybrid technology.  
This research received no external funding.