This project is about detecting defects on steel surface using Unet. The dataset used for this project is the NEU-DET database.

An implementation of the SZZ algorithm, i.e., an approach to identify bug-introducing commits.

we proposed a software defect predictive development models using machine learning techniques that can enable the software to continue its projected task.

Cross-project defection prediction tooling

Defect prediction of java projects using neural networks.

Detection of welding defects with AI (YOLO11)

ICSE'18: Tuning Smote

Software measure datasets of software network structure for defect prediction

Appendix of paper "Within-Project Defect Prediction of Infrastructure-as-Code Using Product and Process Metrics" accepted at Transactions on Software Engineering.

极快速微分催化排序，世界最快的排序算法，The Top Sort 20200317

Mahakil Code

An offline crystal library, which includes about tens of thousand structure calculated by VASP.

A ML model that predicts the number of bugs that might occur while reaching the QA Stage.

Predict the probability of various defects on steel plates.

a project about software prediction

Tuning of parameters of ML algorithms to optimise precision/f-score for fault detection in softwares

Weldright -Techfest repository

Buggyrank is a tool that perform bug prediction by analyzing git repositories.

defect prediction using machine learning

BUGZY - Automated machine learning model to predict if a git commit is a bug fix. Based on topic modeling and natural language processing, it is built with SVM and Latent Dirichlet Allocation (LDA).

Defect prediction guided search-based software testing (SBST-DPG)

Code of Master's thesis Machine Learning for Interactive Performance Prediction

Replication package for Software Defect Prediction Using Rich Contextualized Language Use Vectors

Epsilon domination

Defect prediction in Softwares. The Metrics Data Program dataset provided by NASA has been used.

High-quality datasets designed for Custom ChatGPTs, file-referenced prompting, retrieval-augmented generation (RAG), fine-tuning, and model development to enhance workflows for Testers and Quality Engineers (QEs).

A machine learning-aided bug prediction framework for Java projects combining static code analysis and evolutionary context modeling.

GitHub network mining tool to predict defects in projects.

This repository contains datasets and evaluation scripts for using Large Language Models (LLMs) for static code analysis and defect detection. The project is organized as part of academic research focused on investigating LLMs for identifying code defects.

A fuzzy TOPSIS implementation and its usage for selecting a software defect prediction method