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

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

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.

ICSE'18: Tuning Smote

Software measure datasets of software network structure for defect prediction

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

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

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.

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

a project about software prediction

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

Predict the probability of various defects on steel plates.

Detection of welding defects with AI (YOLO11)

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)

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