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

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

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

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).

Augmenting the Interpretability of GraphCodeBERT for Code Similarity Tasks

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

Improving Source Code Similarity Detection with GraphCodeBERT and Additional Feature Integration

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

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

Django implementation of CodeBERT for detecting vulnerable code.

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)

extracts business-logic code locations.

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.

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).

SpringBoot-based microserviced web app which unmasks, using CodeBERT MLM, a code prompt