software-defect-prediction

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

The implementation of Online Cross-Project JIT-SDP approaches proposed in the paper "Cross-Project Online Just-In-Time Software Defect Prediction" accepted in IEEE Transactions on Software Engineering (TSE), 2022, (accepted).

The defect data set of Solidity Smart Contracts

This repository contains the codes and temporary results used for the analyses for the paper: Liyan Song and Leandro Minku. "A Procedure to Continuously Evaluate Predictive Performance of Just-In-Time Software Defect Prediction Models During Software Development", IEEE Transactions on Software Engineering, 2022

DONE ✅

Software Defect Dataset

Weka implementation of the cost-sensitive decision forest algorithm CSForest.

The project is designed in a componentized manner, and random forests are used in model development.

Agile Testing Using Machine Learning is a project focused on predicting software defects using ML algorithms. The project involves data preprocessing, model training, testing, and accuracy evaluation to improve software testing efficiency, reduce manual effort, and support faster defect detection in Agile development environments.

A Framework for Cross-Project Software Defect Prediction Research

AI-powered software defect prediction using deep learning (LSTM/Transformer) on real-world datasets. Built as part of a Data Science internship project.

An Intelligent Software Defect Prediction System using Generative AI, Spring Boot, FastAPI, React, and Machine Learning.

FSSFO-based computationally efficient wrapper feature selection for software defect prediction using NASA/PROMISE datasets and multiple classifiers.