ai4code

[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

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

[ICLR 2025 Oral] This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with Large Language Models

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

[ICML2025 Oral] LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

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

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

Python action sequences for next-method-call token prediction (Internetware 2022): paper, AST extraction demo, method diagrams, results, and reproducibility guide.

[FORGE 2025] Predicting Program Behavior with Dynamic Dependencies Learning

[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

[ICSE 2026] [ACM SIGSOFT Distinguished Paper Award] SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs

[AAAI 2025] The official code of the paper "InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct"(https://arxiv.org/abs/2407.05700).

[NAACL 2025] Guiding Large Language Models in Code Execution with Fine-grained Multimodal Chain-of-Thought Reasoning

[ACL'2025 Findings] DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation

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.

A selected list of Autoformalization papers, projects, frameworks, libraries, tools, and other resources.