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Recommender Systems (RSs), which generate personalized content, have become a technological tool with diverse applications for users. While numerous RSs have been proposed and successfully implemented across various domains, traditional AI-based RSs still encounter certain challenges, such as data sparsity, cold start, and diversity. Generative Artificial Intelligence in recommender systems is a recent advancement used by platforms like Netflix, Spotify, and Amazon to recommend items, news, videos, audios, goods, and services to their customers/users or to personalize experiences for their customers/users. The main purpose of this review is to compare traditional AI-based recommender systems with generative AI-based recommender systems. A total of fifty-two (52) papers, published between 2019 and February 2024, were selected from six major online libraries. To get a more comprehensive understanding of the selected study, we reviewed the selected studies techniques, and the models, datasets, and metrics used. Our systematic review reveals that generative AI models, such as generative adversarial networks (GANs), variational autoencoder (VAEs) and autoencoders have been widely used in recommender systems and they perform better than traditional AI techniques. Among the 30 datasets analyzed, MovieLens was the most frequently used, accounting for 33%, while Amazon datasets accounted for 11%, Recall and RSME are the most commonly used metrics. Our literature review offers understandings into the Generative AI techniques used across different recommender systems and provides suggestions for the future research. Finally, we elaborated on open issues and discussed current and future trends in generative AI-based recommendation systems.

Software Defect Prediction (SDP) reduces the overall cost of software development by identifying the code at a higher risk of defects at the initial phase of software development. SDP helps the test engineers to optimize the allocation of testing resources more effectively. Traditional SDP models are built using handcrafted software metrics that ignore the structural, semantic, and contextual information of the code. Consequently, many researchers have employed deep learning models to capture contextual, semantic, and structural information from the code. In this article, we propose the DP-CCL (Defect Prediction using CodeBERT with Contrastive Learning) model to predict the defective code. The proposed model employs supervised contrastive learning using this CodeBERT Language model to capture semantic features from the source code. Contrastive learning extracts valuable information from the data by maximizing the similarity between similar data pairs (positive pair) and meanwhile minimizing the similarity between dissimilar data pairs (negative pair). Moreover, The model combines the semantic features with software metrics to obtain the benefits of both semantic and handcrafted features. The combined features are input to the logistic regression model for code classification as either buggy or clean. In this study, ten PROMISE projects were utilized to conduct the experiments. Results show that the DP-CCL model achieved significant improvement i.e., 4.9% to 14.9% increase in F-Score as compared to existing approaches.