[ICML'24] Magicoder: Empowering Code Generation with OSS-Instruct

CodeTF: One-stop Transformer Library for State-of-the-art Code LLM

Inference code and configs for the ReplitLM model family

A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering

Code and Data artifact for NeurIPS 2023 paper - "Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context". `multispy` is a lsp client library in Python intended to be used to build applications around language servers.

Open-source Self-Instruction Tuning Code LLM

[EMNLP 2023] The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation

This repository contains the core methods and models described in the paper “Represent Code as Action Sequence for Predicting Next Method Call.” It uses action sequence modeling to predict method calls in Python code based on developer intentions, treating code editing as a sequence of human-like actions.

multispy is a lsp client library in Python intended to be used to build applications around language servers.

✅SRepair: Powerful LLM-based Program Repairer with $0.029/Fixed Bug

This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with Large Language Models

[ICLR 2021] "Generating Adversarial Computer Programs using Optimized Obfuscations" by Shashank Srikant, Sijia Liu, Tamara Mitrovska, Shiyu Chang, Quanfu Fan, Gaoyuan Zhang, and Una-May O'Reilly

Predicting Program Behavior with Dynamic Dependencies Learning

The official code of the paper "InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct"(https://arxiv.org/abs/2407.05700).

This paper explores the idea of using heterogeneous graph neural networks (Het-GNN) to partition old legacy monoliths into candidate microservices. We additionally take membership constraints that come from a subject matter expert who has deep domain knowledge of the application.

[SANER 2023] "CLAWSAT: Towards Both Robust and Accurate Code Models" by Jinghan Jia*, Shashank Srikant*, Tamara Mitrovska, Chuang Gan, Shiyu Chang, Sijia Liu, Una-May O'Reilly