Search

Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems, particularly through Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and Weight-Decomposed Low-Rank Adaptation (DoRA). RAG integrates external knowledge to enhance factual consistency in generative outputs, while LoRA enables parameter-efficient fine-tuning of large language models (LLMs). DoRA further refines this process by optimizing fine-tuning through adaptive parameter ranking and domain-aware weight adjustments, improving learning efficiency while maintaining inference performance. This paper presents a large-scale empirical evaluation of RAG, LoRA, and DoRA, with model fine-tuning and generation performance assessed on 20,000 FAQ-based queries, while the knowledge base spans 400,000 entries. The study analyzes key performance metrics such as accuracy, relevance, and inference latency. Experimental results demonstrate that DoRA achieves the highest accuracy (90.1%), relevance score (0.88), and lowest latency (110 ms per query), outperforming both LoRA and RAG in real-world, domain-specific generative AI applications. Furthermore, this study examines the trade-offs between fine-tuning efficiency, computational cost, and real-time adaptability across different models. Findings highlight RAG's effectiveness in knowledge grounding, LoRA's cost-efficient domain adaptation, and DoRA's ability to balance fine-tuning efficiency with model precision. These insights provide practical guidance for deploying AI-driven generative systems in accuracy-critical domains such as healthcare, finance, and legal services, ensuring scalability, reliability, and optimal performance in dynamic environments., Comment: 10 Pages

Software testing is a crucial phase in the software development lifecycle (SDLC), ensuring that products meet necessary functional, performance, and quality benchmarks before release. Despite advancements in automation, traditional methods of generating and validating test cases still face significant challenges, including prolonged timelines, human error, incomplete test coverage, and high costs of manual intervention. These limitations often lead to delayed product launches and undetected defects that compromise software quality and user satisfaction. The integration of artificial intelligence (AI) into software testing presents a promising solution to these persistent challenges. AI-driven testing methods automate the creation of comprehensive test cases, dynamically adapt to changes, and leverage machine learning to identify high-risk areas in the codebase. This approach enhances regression testing efficiency while expanding overall test coverage. Furthermore, AI-powered tools enable continuous testing and self-healing test cases, significantly reducing manual oversight and accelerating feedback loops, ultimately leading to faster and more reliable software releases. This paper explores the transformative potential of AI in improving test case generation and validation, focusing on its ability to enhance efficiency, accuracy, and scalability in testing processes. It also addresses key challenges associated with adapting AI for testing, including the need for high quality training data, ensuring model transparency, and maintaining a balance between automation and human oversight. Through case studies and examples of real-world applications, this paper illustrates how AI can significantly enhance testing efficiency across both legacy and modern software systems., Comment: 24 Pages

This paper critically examines the ethical implications of integrating AI tools like GitHub Copilot and ChatGPT into the software development process. It explores issues such as code ownership, bias, accountability, privacy, and the potential impact on the job market. While these AI tools offer significant benefits in terms of productivity and efficiency, they also introduce complex ethical challenges. The paper argues that addressing these challenges is essential to ensuring that AI's integration into software development is both responsible and beneficial to society, Comment: 20 Pages