Awesome-Code-LLM

Discovering Awesome-Code-LLM: Bridging NLP and Software Engineering

Language models for code are rapidly evolving, transcending their initial scopes to bridge various facets of Natural Language Processing (NLP) and Software Engineering (SE). One remarkable effort meticulously dedicated to this domain is the project, Awesome-Code-LLM, which serves as a comprehensive repository and survey on Language Models for Code, curated with a deep understanding of both NLP and SE perspectives.

Understanding the Core

Awesome-Code-LLM is the definitive resource repository associated with a meticulously crafted survey titled "Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code," published in Transactions on Machine Learning Research (TMLR). This project serves as a vital survey and chronologically organized collection of substantial research efforts focused on code-related language model advancements.

News and Updates

The project remains active in bringing the most updated content to its community. For enthusiasts keen on the latest advancements, it features:

Noteworthy Papers: Offering insights into ground-breaking works like OpenAI's GPT-4o System Card, Alibaba's M2rc-Eval, and many more from prestigious institutions like South China Normal University and ByteDance Inc.
Compiled Knowledge: The project has gone the extra mile to compile notable papers and articles in WeChat articles, helping the community effortlessly keep pace with ongoing research trends.

Detailed Survey Structure

The survey is systematically organized into multiple categories, making it accessible and information-rich:

LLM Models and Strategies: Explores foundational and state-of-the-art language models like LaMDA, PaLM, and GPT-4, showcasing pretraining strategies and practical applications of models not solely trained for code but exhibit remarkable coding capabilities.
Application Spectrum: It delves into diverse facets of language models used for coding purposes. From basic code generation to complex tasks like code simulation, interactive coding, and code agents, this survey covers it all, each aspect backed with supporting research.
Specific Language Models: A focused look into models adapted for niche purposes, addressing low-resource, low-level, and domain-specific languages.
Downstream Applications: A comprehensive collection of methods and models employed in downstream tasks such as code ranking, code translation, test generation, and vulnerability detection, extending the efficacy of language models across various software lifecycle stages.

Human-LLM Interactions and Data

A unique inclusion is the section on Human-LLM interactions, which sheds light on integrating human insights and naturally generated language model efficiencies. Also, the project catalogues datasets pertinent for pretraining and benchmarking, fostering an environment of continuous learning and innovation.

Joining the Community

For those interested in contributing to this evolving field, Awesome-Code-LLM invites collaborators to further enrich the repository, positing a collaborative front for advancing code-related language model research.