Abstract Large Language Models (LLMs) have drawn widespread attention and research
due to their astounding performance in text generation and reasoning tasks. Derivative …

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

Large Language Models (LLMs) for code are a family of high-parameter, transformer-based
neural networks pre-trained on massive datasets of both natural and programming …

Recommending APIs is a practical and essential feature of IDEs. Improving the accuracy of
API recommendations is an effective way to improve coding efficiency. With the success of …

Code clone detection is an important aspect of software development and maintenance. The
extensive research in this domain has helped reduce the complexity and increase the …

Transformers have revolutionized natural language processing (NLP) and have had a huge
impact on automating tasks. Recently, transformers have led to the development of powerful …

Traditional software effort estimation methods, such as term frequency–inverse document
frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they …

The most severe problem in cross-programming languages is feature extraction due to
different tokens in different programming languages. To solve this problem, we propose a …

Developers often search and reuse existing code snippets to improve software development
efficiency during software development. Currently, researchers have proposed many code …

AUTOMATIC SPEECH RECOGNITION and TRANSLATION for LOW-RESOURCE
LANGUAGES This book is a comprehensive exploration into the cutting-edge research …