This study explores the effectiveness and efficiency of the popular OpenAI model ChatGPT, powered by GPT-3.5 and GPT-4, in programming tasks to understand its impact on programming and potentially software development. To measure the performance of these models, a quantitative approach was employed using the Mostly Basic Python Problems (MBPP) dataset. In addition to the direct assessment of GPT-3.5 and GPT-4, a comparative analysis involving other popular large language models in the AI landscape, notably Google’s Bard and Anthropic’s Claude, was conducted to measure and compare their proficiency in the same tasks. The results highlight the strengths of ChatGPT models in programming tasks, offering valuable insights for the AI community, specifically for developers and researchers. As the popularity of artificial intelligence increases, this study serves as an early look into the field of AI-assisted programming.

It is hard to trust any data entry on online websites as some websites may be malicious, and gather data for illegal or unintended use. For example, bank login and credit card information can be misused for financial theft. To make users aware of the digital safety of websites, we have tried to identify and learn the pattern on a dataset consisting of features of malicious and benign websites. We treated the problem of differentiation between malicious and benign websites as a classification problem and applied several machine learning techniques, for example, random forest, decision tree, logistic regression, and support vector machines to this data. Several evaluation metrics such as accuracy, precision, recall, F1 score, and false positive rate, were used to evaluate the performance of each classification technique. Since the dataset was imbalanced, the machine learning models developed a bias during training toward a specific class of websites. Multiple data balancing techniques, for example, undersampling, oversampling, and SMOTE, were applied for balancing the dataset and removing the bias. Our experiments showed that after balancing the data, the random forest algorithm using the oversampling technique showed the best results in all evaluation metrics for the benign and malicious website feature dataset.

This comparative analysis delves into the capabilities of three prominent Conversational AI models: ChatGPT, Google Bard, and Microsoft Bing Chat. The study encompasses a meticulous exploration of their conversational skills, natural language processing abilities, and creative text generation. Methodologically, this study crafts a comprehensive evaluation framework, including complexity levels and tasks for each dimension. Through user-generated responses, key metrics of fluency, coherence, relevance, accuracy, completeness, informativeness, creativity, and relevance were assessed. The results reveal distinctive strengths of the AI models. The theoretical implications lead to recommendations for dynamic learning, ethical considerations, and cross-cultural adaptability. Practically, avenues for future research were proposed, including real-time user feedback integration, multimodal capabilities exploration, and collaborative human-AI interaction studies. The analysis sets the stage for benchmarking and environmental impact assessments, underlining the need for standardized metrics.

This work builds a benchmarking pipeline for resource deployment, configuration input, and the evaluation of Cassandra and ScyllaDB performance. It investigates performance under different scenarios, workload types, and internal structures. Insights and future improvements are provided. Analysing comparisons of both databases, there were several notable differences between Cassandra and ScyllaDB. ScyllaDB demonstrated superior performance in production-ready materialized views, global secondary indices, lightweight transactions, change data capture, row-based data cache, and adaptive behaviour to real-world workloads. On the other hand, Cassandra exhibited advantages, such as a well-established size-tiered compaction approach and the ability to leverage existing Java Virtual machine (JVM) tuning techniques.