QueryFuzz implements a metamorphic testing approach to test Datalog engines.

Related tags

Deep LearningqueryFuzz
Overview

Python package Python application

Datalog is a popular query language with applications in several domains. Like any complex piece of software, Datalog engines may contain bugs. The most critical ones manifest as incorrect results when evaluating queries (query bugs). Given the wide applicability of the language, query bugs may have detrimental consequences, for instance, by compromising the soundness of a program analysis that is implemented and formalized in Datalog.

QueryFuzz implements the metamorphic testing approach for Datalog engines described in:

M. N. Mansur, M. Christakis, V. Wüstholz - Metamorphic Testing of Datalog Engines -
In Proceedings of the 29th Joint European Software Engineering Conference and Symposium on 
the Foundations of Software Engineering (ESEC/FSE'21).

Installation:

Ubuntu/Debian:

Support for C++17 is required, which is supported in g++ 7/clang++ 7 on.

sudo apt-get install autoconf automake bison build-essential clang doxygen flex g++ git libffi-dev libncurses5-dev libtool libsqlite3-dev make mcpp python sqlite zlib1g-dev
git clone https://github.com/numairmansur/queryFuzz
virtualenv --python=/usr/bin/python3.7 venv
source venv/bin/activate
cd queryFuzz
python setup.py install

Usage:

Testing Soufflé:

You can immediately start testing Soufflé by just typing the following command:

queryfuzz

When you run this command for the first time, it will download and install Soufflé. We use Soufflé as our backend tool to compare and find discrepancies in the results of two Datalog programs. After successfully installing Soufflé, the above command will start the fuzzing procedure on the latest revision of Soufflé.

If you want to test a different version of Soufflé, please build and install that version and paste the path to Soufflé executable in the path_to_souffle_engine field in file /path/to/queryFuzz/params.json.

Testing µZ:

If you want to run queryFuzz on µZ, please first build and install the appropriate version of z3. Then paste the path to z3 executable in the path_to_z3_engine field in file /path/to/queryFuzz/params.json. You can then begin the fuzzing procedure by running:

queryfuzz --engine=z3

Testing DDlog:

If you want to run queryFuzz on DDlog, please first build and install the appropriate version of DDlog. Then paste the path to DDlog executable in the path_to_ddlog_engine field in file /path/to/queryFuzz/params.json. You would also have to add path to DDlog home directory in the path_to_ddlog_home_dir field in /path/to/queryFuzz/params.json. You can then begin the fuzzing procedure by running:

queryfuzz --engine=ddlog

Want to test your own Datalog engine?

If you want to use QueryFuzz to test your own Datalog engine, please get in touch at [email protected].

Running on multiple cores:

If you wish to run parallel instances of Queryfuzz on n cores, use the --cores flag. For example:

queryfuzz --cores=n

Reproducing query bugs reported in our ESEC/FSE'21 paper:

Please follow the instructions here.

You might also like...
Implements MLP-Mixer: An all-MLP Architecture for Vision.
Implements MLP-Mixer: An all-MLP Architecture for Vision.

MLP-Mixer-CIFAR10 This repository implements MLP-Mixer as proposed in MLP-Mixer: An all-MLP Architecture for Vision. The paper introduces an all MLP (

Sayak Paul 51 Jan 4, 2023
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 9, 2022
This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong Poisons
This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong Poisons

Adversarial poison generation and evaluation. This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong

null 31 Nov 1, 2022
Implements an infinite sum of poisson-weighted convolutions

An infinite sum of Poisson-weighted convolutions Kyle Cranmer, Aug 2018 If viewing on GitHub, this looks better with nbviewer: click here Consider a v

Kyle Cranmer 26 Dec 7, 2022
Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)
Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation) Download Synthia dataset The model uses

null 32 Sep 21, 2022
FastReID is a research platform that implements state-of-the-art re-identification algorithms.
FastReID is a research platform that implements state-of-the-art re-identification algorithms.

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

JDAI-CV 2.8k Jan 7, 2023
A mini lib that implements several useful functions binding to PyTorch in C++.

Torch-gather A mini library that implements several useful functions binding to PyTorch in C++. What does gather do? Why do we need it? When dealing w

maxwellzh 8 Sep 7, 2022
This implements one of result networks from Large-scale evolution of image classifiers
This implements one of result networks from Large-scale evolution of image classifiers

Exotic structured image classifier This implements one of result networks from Large-scale evolution of image classifiers by Esteban Real, et. al. Req

null 54 Nov 25, 2022
Implements pytorch code for the Accelerated SGD algorithm.

AccSGD This is the code associated with Accelerated SGD algorithm used in the paper On the insufficiency of existing momentum schemes for Stochastic O

null 205 Jan 2, 2023
Releases(fse_repl)
Owner
Maria Christakis' research group at MPI-SWS
GitHub Repository
Official source code to CVPR'20 paper, "When2com: Multi-Agent Perception via Communication Graph Grouping"

When2com: Multi-Agent Perception via Communication Graph Grouping This is the PyTorch implementation of our paper: When2com: Multi-Agent Perception vi

34 Nov 09, 2022
Python script to download the celebA-HQ dataset from google drive

download-celebA-HQ Python script to download and create the celebA-HQ dataset. WARNING from the author. I believe this script is broken since a few mo

133 Dec 21, 2022
Code basis for the paper "Camera Condition Monitoring and Readjustment by means of Noise and Blur" (2021)

Camera Condition Monitoring and Readjustment by means of Noise and Blur This repository contains the source code of the paper: Wischow, M., Gallego, G

7 Dec 22, 2022
Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Exercises and project documentation for the 3. Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Simona Mircheva 1 Jan 13, 2022
[NeurIPS-2021] Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

Efficient Graph Similarity Computation - (EGSC) This repo contains the source code and dataset for our paper: Slow Learning and Fast Inference: Effici

23 Nov 11, 2022
HAT: Hierarchical Aggregation Transformers for Person Re-identification

HAT: Hierarchical Aggregation Transformers for Person Re-identification

11 Sep 05, 2022
LogAvgExp - Pytorch Implementation of LogAvgExp

LogAvgExp - Pytorch Implementation of LogAvgExp for Pytorch Install $ pip instal

Phil Wang 31 Oct 14, 2022
STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack This is the official implementation of the paper: STMTrack: Template-free Visual Tracking with Space-time Memory Networks. Setup Prepare Anac

Zhihong Fu 62 Dec 21, 2022
Can we do Customers Segmentation using PHP and Unsupervized Machine Learning ? Yes we can ! 🤡

Customers Segmentation using PHP and Rubix ML PHP Library Can we do Customers Segmentation using PHP and Unsupervized Machine Learning ? Yes we can !

Mickaël Andrieu 11 Oct 08, 2022
How to train a CNN to 99% accuracy on MNIST in less than a second on a laptop

Training a NN to 99% accuracy on MNIST in 0.76 seconds A quick study on how fast you can reach 99% accuracy on MNIST with a single laptop. Our answer

Tuomas Oikarinen 42 Dec 10, 2022
Pure python implementations of popular ML algorithms.

Minimal ML algorithms This repo includes minimal implementations of popular ML algorithms using pure python and numpy. The purpose of these notebooks

Alexis Gidiotis 3 Jan 10, 2022
Transformer in Vision

Transformer-in-Vision Recent Transformer-based CV and related works. Welcome to comment/contribute! Keep updated. Resource SCENIC: A JAX Library for C

Yong-Lu Li 1.1k Dec 30, 2022
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation (NeurIPS2021 Benchmark and Dataset Track)

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Kingdrone 174 Dec 22, 2022
7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle

kaggle-hpa-2021-7th-place-solution Code for 7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle. A description of the met

8 Jul 09, 2021
WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking

WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking [Paper Link] Abstract In this work, we contribute a new million-scale Un

25 Jan 01, 2023
ROS Basics and TurtleSim

Waypoint Follower Anna Garverick This package draws given waypoints, then waits for a service call with a start position to send the turtle to each wa

Anna Garverick 1 Dec 13, 2021
A PyTorch implementation of deep-learning-based registration

DiffuseMorph Implementation A PyTorch implementation of deep-learning-based registration. Requirements OS : Ubuntu / Windows Python 3.6 PyTorch 1.4.0

24 Jan 03, 2023
This game was designed to encourage young people not to gamble on lotteries, as the probablity of correctly guessing the number is infinitesimal!

Lottery Simulator 2022 for Web Launch Application Developed by John Seong in Ontario. This game was designed to encourage young people not to gamble o

John Seong 2 Sep 02, 2022
Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting (HMNet)

Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting (HMNet) Our paper: https://arxiv.org/abs/2111.13324 We will release the complet

15 Oct 17, 2022
The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020

TDANet: Text-Guided Neural Image Inpainting, MM'2020 (Oral) MM | ArXiv This repository implements the paper "Text-Guided Neural Image Inpainting" by L

LisaiZhang 75 Dec 22, 2022