# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
## 1 Introduction  
People are depending more and more on software systems in their daily lives due to the growth of computer science, software development technology, and digital information technology, which raise the standards for software quality [1; 2; 3]. Software system failure results from defects in the system, which puts the security of the system in grave danger [4; 5]. Software defect prediction (SDP) technology, however, can make it easier for testers and software system developers to find bugs in software. As a result, in this context, comprehensive study of SDP technology is becoming increasingly crucial. SDP aims to assist software engineers in allocating scarce resources to enhance the quality of software products by devising an efficient method for predicting the errors in a particular software project [6; 7; 8]. Over time, numerous methods have been put forth and used in software development, assisting practitioners in allocating limited testing resources to modules that frequently exhibit defects. Early studies concentrated on within-project defect prediction (WPDP), which learned the SDP model from previous data from the same project and then used it to predict defects in the releases that were coming soon [9; 10; 11]. The SDP model's preliminary research indicates that, if there are adequate sample data from the same project, learning-derived prediction performance will be effective in the same project. Numerous software projects with adequate sample data have been kept for long-term software development and research. As a result, researchers naturally consider using the sample data from well-known software projects in order to learn the model and apply it to predict defects in other software projects, which is the cross-project defect prediction (CPDP) model's design principle [12; 13; 14; 15]. Several CPDP models have been presented recently, and many academics have taken an interest in them [16; 17; 18]. In the realm of software defect prediction, machine learning approaches, such as decision trees and neural networks, have been successful, as evidenced by the literature, which also found that these models performed well [19; 20; 21]. However, there is still room for improvement in CPDP's predictive accuracy by minimizing distribution differences and class imbalance difficulties. There is a problem of class imbalance if there is a tendency towards considerably fewer modules with defects than there are modules without defects [22]. Because CPDP models may favor the majority while classifying, the class imbalance issue may have an impact on their performance [23]. As a result, creating strategies to successfully address the class imbalance issue in software projects is a common area of study in CPDP. Numerous studies [24; 25; 26] have either suggested or assessed various strategies. When there is imbalance and overlap in the data, the prediction power decreases. However, the existence of noisy and irrelevant instances among source and target projects has a bigger impact on the prediction performance of the CPDP models than class imbalance, which is not inherently problematic. On the other hand, when projecting the target project, the prediction model constructed using the pertinent data from the source project is unable to produce the best possible prediction performance. The primary cause is the significant difference in data distribution between the target and source projects. The distribution of features between projects and variations between instances account for the majority of the variation in data distribution. In the past, academics believed that the distributions of the source and target projects would be the same. In actuality, the source data for these projects are inherently distributed differently, since the projects created by various teams and businesses are invariably distinct in terms of scope, function, and coding standards. In other words, the distribution of data may vary throughout projects.