CodeBERTScore: an automatic metric for code generation, based on BERTScore

EVIL (Exploiting software VIa natural Language) is an approach to automatically generate software exploits in assembly/Python language from descriptions in natural language. The approach leverages Neural Machine Translation (NMT) techniques and a dataset that we developed for this work.

Neural search engine for discovering semantically similar Python repositories on GitHub

Repository about small code models

Augmenting the Interpretability of GraphCodeBERT for Code Similarity Tasks

🕵️‍♂️ ML project to identify malicious web payloads, aimed at boosting the effectiveness of WAFs and IDSs.

This repository contains the code, the dataset and the experimental results related to the paper "Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks" accepted for publication at The 32nd IEEE/ACM International Conference on Program Comprehension (ICPC 2024).

Code of our paper "Method-Level Bug Severity Prediction using Source Code Metrics and LLMs" which is accepted to ISSRE 2023.

Advanced Detection of Source Code Clones via an Ensemble of Unsupervised Similarity Measures

Improving Source Code Similarity Detection with GraphCodeBERT and Additional Feature Integration

Fine-tuning CodeBERT for Vulnerability Detection

Fine-tuning CodeBERT with AST-based Vectors for Code Translation

This repository contains experiments on comparing the similarity of Python repositories using ML models.

Performs Code Summarization, Bug Detection, Bug Removal using different Natural language processing models including Garph CodeBERT, GREAT, GNN, CoText etc.

A project for determining the similarity of python repositories based on embedding approach

CodeOpt: A framework for optimizing code performance using Two-Stage Sampling, Few-Shot Learning, and Iterative Self-Reflection with support for Genetic Algorithm Inspired Chain-of-Thought (GA-COT).

CodeBERT + LoRA fine-tuning for C/C++ vulnerability detection | F1 = 74.3% | PyTorch, HuggingFace Transformers, PEFT

No description provided.

The modern web development landscape is plagued by a peculiar paradox: despite the abundance of UI components and design systems, developers still spend countless hours reimplementing similar interfaces. S0 addresses this challenge by introducing a novel approach that combines advanced vector search capabilities.

Django implementation of CodeBERT for detecting vulnerable code.

"AI-powered vulnerability detection for Solidity smart contracts using Mistral + CodeBERT"

extracts business-logic code locations.

Auto-grading of C programs using Machine Learning and Deep Learning models such as random forest, CNN, LSTM etc and code embedding models such as CodeBERT. Also published a paper for the same in IEEE (14th ICCNT Conference)

A deterministic and neuro-symbolic framework for evaluating LLM-generated code using Abstract Syntax Trees, Semantic Embeddings, and Integrated Gradients. Think of it as a 'Digital Polygraph' for AI. It uses a three-step verification process to ensure the AI didn't 'misunderstand' your instructions

This study compares three transformer-based mod- els—CodeT5, CodeBERT, and CodeGen.

The study uses the IRSE/FIRE dataset and explores the impact of combining original C code data with Python-derived silver-standard

🤖 Generate tailored AI training datasets quickly and easily, transforming your domain knowledge into essential training data for model fine-tuning.

Implementation and dataset for A Zero-Shot Framework for Cross-Project Vulnerability Detection in Source Code (Empirical Software Engineering, 2026).

CodeXGLUE, a benchmark dataset to foster machine learning research for program understanding and generation.

This repository is source code of conference paper "From Bug Reports to Code Quality: A Transformer-Based Classification Approach"