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arm-testoracle-extention

arm-testoraclegeneration

we developed a method which generates test oraclesby comparing information extracted from object state data created during the execution of two subsequent versions of the SUT

blitz

Blitz++ Multi-Dimensional Array Library for C++

bugsinpy

BugsInPy: Benchmarking Bugs in Python Projects

clustering_microbial-growth

yeasts are important cell factories for the transition towards more sustainable industrial processes. Their physiological characterization in bioreactors generates large amounts of data that is manually analyzed by the scientists to find patterns and cluster the growth into different phases. The development of improved tools for efficient and accurate data analysis is essential for bioprocesses. We want to present a tool that can organize, cluster, and derive meaningful patterns from the physiological characterization of microbial cell factories.

colt-javalibrary

Original source of CERN Colt library, as the homepage https://dst.lbl.gov/ACSSoftware/colt/ doesn't contain it anymore

datasharing

The Leek group guide to data sharing

deepdiveinto_memo-maltesque22

defects4j

A Database of Real Faults and an Experimental Infrastructure to Enable Controlled Experiments in Software Engineering Research

exploring-atheris

I forked this repo for reproducing its examples

flask-hello-world

Flask Hello World Example for Render

flutter-quizstar

This is a Quiz App With Timer In Flutter

flutterapp

This is a small application, to get started in the dart programming language and the Flutter framework.

fuzzingbook

Project page for "The Fuzzing Book"

fuzzingexp-googleoss-fuzz

This repo explores Google OSS-Fuzz

kmeans_python

El K-means es un método de Clustering que separa ‘K’ grupos de objetos (Clusters) de similar varianza, minimizando un concepto conocido como inercia

memo

metaexplorex-logs

This repository contains log files and example data for the MetaExploreX tool, a visualisation and exploration tool designed for analysing the outcomes of metamorphic testing.

metaexplorex-masterthesis

metatrimmer

mrs-where2findthem

Metamorphic Relations and Where to Find Them :)

mypublications

numpy

The fundamental package for scientific computing with Python.

phdstuff_repo

predicting-survived-and-killed-mutants

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

pyc-cfg

Pyc-cfg is a pure python control flow graph builder for almost all Ansi C programming language.

pynguin

The PYthoN General UnIt Test geNerator is a test-generation tool for Python

reimagined-robot

This was a Udemy course for C++, "C++ Crash Course for beginners who want to learn C++ in less than 2 hours!"

rene-predictingmetamorphicrelations

Metamorphic Test (MT) is a software testing technique that addresses the test oracle issue. It differs from traditional testing techniques in that it looks at the relations between the inputs and outputs of different test cases executions rather than specific test results. Such relations are known as Metamorphic Relationships (MRs), and they are the MT core aspect. In MT, testers may indirectly test the System Under Test (SUT) by looking at whether the inputs and outputs meet the MRs. If a particular MR is not violated, it does not guarantee that the program will be implemented correctly. However, if an MR is violated for certain test cases, then there must be a fault in the SUT. MRs are currently discovered manually, which necessitates a thorough grasp of the SUT and the application domain. As a result, MT might take a long time and be prone to errors. To mitigate this problem, the metamorphic relationship prediction (PMR) approach was proposed. PMR uses a classification model based on features gathered from the SUT source code, at the method level, to predict whether a new method would exhibit a particular predefined MR. The goal of our research is to investigate how effectively the suggested PMR approach, which has been tested on Java methods, may be applied to other programming languages. To do this, we first replicated the prior work and then expanded the PMR to Python and C++.