Modern code generation tools use AI models, particularly Large Language Models (LLMs),
to generate functional and complete code. While such tools are becoming popular and …

Large-scale language models (LLMs) have emerged as a groundbreaking innovation in the
realm of question-answering and conversational agents. These models, leveraging different …

Large Language Models (LLMs) have shown promise in automated code generation but
typically excel only in simpler tasks such as generating standalone code units. Real-world …

AI powered code-recommendation systems, such as Copilot and CodeWhisperer, provide
code suggestions inside a programmer's environment (eg, an IDE) with the aim of improving …

Software specifications are essential for ensuring the reliability of software systems. Existing
specification extraction approaches, however, suffer from limited generalizability and require …

The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

Programmers increasingly rely on Large Language Models (LLMs) for code generation.
However, they now have to deal with issues like having to constantly switch between …

Generative AI models, specifically large language models (LLMs), have made strides
towards the long-standing goal of text-to-code generation. This progress has invited …

Modern language models (LMs) have been successfully employed in source code
generation and understanding, leading to a significant increase in research focused on …

Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for
general, wide-ranging purposes. GM content filters are generalized to filter out content which …