## Abstract  
The objective of cross-project defect prediction (CPDP) is to develop a model that trains bugs on current source projects and predicts defects of target projects. Due to the complexity of projects, CPDP is a challenging task, and the precision estimated is not always trustworthy. Our goal is to predict the bugs in the new projects by training our model on the current projects for cross-projects to save time, cost, and effort. We used experimental research and the type of research is explanatory. Our research method is controlled experimentation, for which our independent variable is prediction accuracy and dependent variables are hyper-parameters which include learning rate, epochs, and dense layers of neural networks. Our research approach is quantitative as the dataset is quantitative. The design of our research is 1F1T (1 factor and 1 treatment). To obtain the results, we first carried out exploratory data analysis (EDA). Using EDA, we found that the dataset is multi-class. The dataset contains 11 different projects consisting of 28 different versions of all the projects in total. We also found that the dataset has significant issues of noise, class imbalance, and distribution gaps between different projects. We pre-processed the dataset for experimentation by resolving all these issues. To resolve the issue of noise, we removed duplication from the dataset by removing redundant rows. We then covered the data distribution gap between different sources and target projects using the min-max normalization technique. After covering the data distribution gap, we generated synthetic data using a CTGANsynthesizer to solve class imbalance issues. We solved the class imbalance issue by generating an equal number of instances, as well as an equal number of output classes. After carrying out all of these steps, we obtained normalized data. We applied the hybrid feature selection technique on the pre-processed data to optimize the feature set. We obtained significant results of an average AUC of 75.98%. From the empirical study, it was demonstrated that feature selection and hyper-parameter tuning have a significant impact on defect prediction accuracy in cross-projects. [1][PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Impact of Optimal Feature Selection Using Hybrid Feature Selection Using  
is known as within-project defect prediction (WPDP) and it has been studied widely [3]. Using available public datasets, one can discover the practicality of models created for different tasks, particularly for people with partial or no defect records repository. Most traditional software defect prediction models focus on within project defect prediction (WPDP) and the major limitations of WPDP is lack of training information in the early phases of the software development and testing process. Therefore, the proposed feature selection based prediction model is used to assess the cross project defect prediction (CPDP) on an open source repository [4]. In the latest studies, models of machine learning are successfully implemented to identify defective features, e.g., correlation analysis, neural networks, and deep learning (DL). Previous studies have shown that by using suitable and complete data, one can train a useful machine-learning model. However, it is a crucial problem that an upcoming project with partial historical data could result in better prediction model performance. In the absence of adequate amounts of historical data, cross-project defect prediction [1,2] is an acceptable answer, which discusses the development of the model trained by learning source projects which have defects, and the prediction is then carried out on target projects. However, attaining a satisfactory CPDP is stimulating. [PERSON] et al. estimated CPDP performance on 12 projects (622 cross-project pairs). Among them, 21 total pairs achieved well. The data from different projects usually have an enormous distribution gap that interrupts an elementary requirement of many of the ML technologies, i.e., similar distribution gaps.