Gu-Youngfeng

Gu-youngfeng's repositories

CraTer

CraTer is an open-source Java tool which is the prototype in paper "Does the Fault Reside in a Stack Trace?"

Language:JavaApache-2.07 20

CrashOutlier

Using Weka to conduct outlier detection experiments

Language:Java3 10

EfficiencyMiner

This project explores the efficiency of CraTer in terms of time consumption and line efforts reduction.

Language:JavaApache-2.03 3 6

BehaviorDection

Operation mining in Event interaction dataset

Language:JavaApache-2.02 2 4

Config-Optimization

This project includes implementations of different configuration optimization methods in prior papers.

Language:PythonApache-2.02 3 1

CraTer-Tool

CraTer-Tool is a light-weight crash localization tool for Java programs, it analyze the crash information and predict whether or not the crashing-fault reside inside the stack trace.

Language:Java2 20

Gu-Youngfeng.github.io

Personal website of Yongfeng Gu

Language:HTMLApache-2.02 10

PATool

Give some example of usage of some Program Analysis Tools

Language:JavaApache-2.02 2 1

SamplingAlgorithms

Configuration sampling algorithms with constraints.

Language:JavaApache-2.02 10

CARTextend

Java Implementation of CARTree Algorithm in paper "Variability-Aware Performance Prediction: A Statical Learning Approach"

Language:JavaApache-2.01 20

LASCA_CODE

This repository includes the source code of LASCA approach.

Language:Python100

MarkDownEditor

This project saved my MarkDown file as well as pics.

1 2 1

MoConfig

This project offers the source code of prototype MoConfig.

Language:Python1 30

ReConfigSRC

ReConfig is a post-processing approach to improve the ranking accuracy of the rank-based approach.

Language:Python1 2 4

RRepository

This projects provides my learning process on R language as well as some practical R projects.

Language:RApache-2.01 20

crater-tool-vscode

We developed the CraTer extension for Visual Studio Code.

Language:TypeScript020

autoconfig

The source code for ASE 2018 paper: AutoConfig: Automatic Configuration Tuning for Distributed Message Systems

Language:Java010

DeepPerf is an end-to-end deep learning based solution that can train a software performance prediction model from a limited number of samples and predict the performance value of a new configuration.

Language:Python000

Distance-Based_Data

Language:PythonGPL-2.0010

Flash-SingleConfig

Single objective performance optimization for highly configurable systems

Language:Python010

geatpy

Evolutionary algorithm toolbox and framework with high performance for Python

Language:PythonLGPL-3.0010

LearningTensorflow

Some codes during the study process.

Language:Python01 9

ParallelDebugging

Providing some algorithms, including BDM, Jones II, W-K, and MSP algorithms, in parallel debugging.

010

SPLConqueror

SPL Conqueror is a library to learn the influence of configuration options of configurable software systems on non-functional properties.

Language:C#GPL-2.0010

TenSamplingStudy1

This project is cloned from the open-source implementation in paper "A Comparison of 10 Sampling Algorithms for Configurable Systems." This Study1 implements the sampling algorithms on ideal condition (non-constraints).

Language:CApache-2.0010

TenSamplingStudy2

This project is cloned from the open-source implementation in paper "A Comparison of 10 Sampling Algorithms for Configurable Systems." This Study2 implements the sampling algorithms considering constraints.