awsm-research/DeepLineDP

Stars
27
Rank 905,827 (Top 18 %)
Language
Python
Created about 3 years ago
Updated about 3 years ago

awsm-research/DeepLineDP

awsm-research

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LineVul

A Transformer-based Line-Level Vulnerability Prediction

VulRepair

VulRepair: A T5-Based Automated Software Vulnerability Repair

AIBugHunter

AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities

PyExplainer

PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

line-level-defect-prediction

A3Test

Rnalytica

Rnalytica: An R package of JIRA defect datasets and tool suites for actionable software analytics

VulExplainer

VulExplainer: A Transformer-based Hierarchical Distillation for Explaining Vulnerability Types

tutorial

Software Analytics in Action: A Hands-on Tutorial on Mining, Analyzing, Modelling, and Explaining Software Data

Jupyter Notebook

VQM

Vision Transformer-Inspired Automated Vulnerability Repair

ChatGPT4Vul

pycoder

Jupyter Notebook

AutoTransform-Replication

gpt2sp

Jupyter Notebook

Large-Defect-Prediction-Benchmark

JITLine-replication-package

JIT-defect-models

replication-icse2019

Replication Package: Mining Software Defects: Should We Consider Affected Releases?

LLM-for-code-review-automatiton

gpt2sp_webapp

storypoints-changes

This is a replication package for the paper titled "Understanding and Predicting the change of StoryPoints"

wiki-admin

SQAPlanner-implementation

paper-writing-guidelines

optimatch

Optimal Transport for Function-Level and Line-Level Vulnerability Detection

D-ACT-Replication-Package

DefectData

The R package DefectData is a collection of software metrics dataset for software engineering researchers.