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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
Code of our paper "Method-Level Bug Severity Prediction using Source Code Metrics and LLMs" which is accepted to ISSRE 2023.
🕵️♂️ ML project to identify malicious web payloads, aimed at boosting the effectiveness of WAFs and IDSs.
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
extracts business-logic code locations.
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).
Advanced Detection of Source Code Clones via an Ensemble of Unsupervised Similarity Measures
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)
Fine-tuning CodeBERT MLM on Java-based enterprise projects
Neural search engine for questions/answers from StackOverflow
Django implementation of CodeBERT for detecting vulnerable code.
CodeXGLUE, a benchmark dataset to foster machine learning research for program understanding and generation.
Reproducibility report ofCoSQA: 20,000+ Web Queries for Code Search and QuestionAnswering for ML Reproducibility Challenge 2021