Aleja Duque-Torres's repositories
BugsInPy
BugsInPy: Benchmarking Bugs in Python Projects
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
MRs-Where2findThem
Metamorphic Relations and Where to Find Them :)
numpy
The fundamental package for scientific computing with Python.
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++.
RLStuff
A collection of reinforcement learning algorithm implementations
SCminer
Extracting static features from source code
test-amplification-SEAA2023
Provides code and results evaluating an approach that generates amplified regression tests.
Useful-codes
Some really useful codes ;)
VST2023-BugORNOTbug
This repo contains the full set of data generated during the experiments performed for the paper Bug or not Bug? Analysing the Reasons Behind Metamorphic Relation Violations, as well as all scripts
vsTodoPlugin
Personalised vsCode extension