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9 results for “black-box modelling”

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zenodo40/100

Making the Most out of a Hydrological Model Dataset: Sensitivity Analyses to Open the Model Black-Box (data and code)

This is "data and code" repository for the Water Resources Research Article 2017WR020401 by Borgonovo et al. (2017): "Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box". Each sub-directory contains the Matlab or R scripts to reproduce all paper plots. Note, that the data of this repository (i.e. under ./data_input ) are identical to the data analysed by Rakovec et al. (2014). References: <ul> <li>Borgonovo, E., Lu, X., Plischke, E., Rakovec, O. and Hill, M. C. (2017), Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box. Water Resour. Res.. Accepted Author Manuscript. doi:10.1002/2017WR020767</li> <li>Rakovec, O., M. C. Hill, M. P. Clark, A. H. Weerts, A. J. Teuling, and R. Uijlenhoet (2014), Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, doi:10.1002/2013WR014063.</li> </ul>

opencc-by-4.0Sep 2017View details →

zenodo36/100

Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests – Replication Package

This is the replication package associated with the paper: Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests. We explain how to use it to reproduce the results reported in the paper. A maintainable version of this replication package is available on GitHub (<a href="https://github.com/uOttawa-Nanda-Lab/Flakify">https://github.com/uOttawa-Nanda-Lab/Flakify</a>). Flakify Test Smell Detector This is a step-by-step guideline to detect test smells in the source code of test cases and retain statements that match them. Requirements: <ul> <li>Eclipse IDE (the version we used was 2021-12)</li> <li>The libraries (the .jar files in the <code>lib\</code> directory)</li> </ul> Input Files: This is a list of input files that are required to accomplish this step: <ul> <li> dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv </li> <li> dataset/FlakeFlagger/FlakeFlagger_class_files/ </li> <li> dataset/IDoFT/IDoFT_filtered_dataset.csv </li> <li> dataset/IDoFT/IDoFT_class_files/ </li> </ul> The <code>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</code> and <code>dataset/IDoFT/IDoFT_filtered_dataset.csv</code> are used to obtain the label (flaky=1 or non-flaky=0) and project name for each test case parsed from <code>dataset/FlakeFlagger/FlakeFlagger_class_files/</code> and <code>dataset/IDoFT/IDoFT_class_files/</code>, respectively. Output Files: <ul> <li> dataset/FlakeFlagger/FlakeFlagger_dataset.csv </li> <li> dataset/FlakeFlagger/FlakeFlagger_test_cases_full_code/ </li> <li> dataset/FlakeFlagger/FlakeFlagger_test_cases_preprocessed_code/ </li> <li> dataset/IDoFT/IDoFT_dataset.csv </li> <li> dataset/IDoFT/IDoFT_test_cases_full_code/ </li> <li> dataset/IDoFT/IDoFT_test_cases_preprocessed_code/ </li> </ul>   Replicating the experiment To detect test smells and retain only code statements related to them, the <code>src/FlakifySmellsDetector.java</code> file should be compiled and run using the Eclipse IDE by having all the .jar files in the classpath. The pre-generated executable Jar file <code>src/FlakifySmellsDetector.jar</code> can be executed using the shell script <code>src/FlakifySmellsDetector.sh</code> after changing paths for each dataset as needed, using the following commands: <pre><code class="language-bash">bash FlakifySmellsDetector.sh FlakeFlagger bash FlakifySmellsDetector.sh IDoFT</code></pre> It will generate the dataset required to run Flakify's flaky test prediction model for the datasets given as input. The class file containing each of the test cases is then parsed to produce the corresponding full code and pre-processed code of the test case. The full and pre-processed source code of all test cases are also combined and saved in a CSV file, along with test smells found, project names, and labels.   Flakify Replication This is the guideline for replicating the experiments we used to evaluate Flakify for classifying test cases as flaky and non-flaky using both cross-validation and per-project validation. Requirements: This is a list of all required python packages: <ul> <li>python =3.8.5</li> <li>imbalanced_learn= 0.8.1</li> <li>numpy= 1.19.5</li> <li>pandas= 1.3.3</li> <li>transformer= 4.10.2</li> <li>torch=1.5.0</li> <li>scikit_learn= 0.22.1</li> </ul> Input Files: This is a list of input files that are required to accomplish this step: <ul> <li>dataset/FlakeFlagger/Flakify_FlakeFlagger_dataset.csv</li> <li>dataset/IDoFT/Flakify_IDoFT_dataset.csv</li> </ul> This file contains the full code and pre-processed code of the test cases in both FlakeFlagger and IDOFT datasets, along with their ground truth labels (flaky and non-flaky). Output File: <ul> <li> results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv </li> <li> results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv </li> <li> results/Flakify_model_trained_on_FlakeFlagger_dataset.pt </li> <li> results/Flakify_cross_validation_results_on_IDoFT_dataset.csv </li> <li> results/Flakify_per_project_results_on_IDoFT_dataset.csv </li> <li> results/Flakify_model_trained_on_IDoFT_dataset.pt </li> </ul>   Replicating Flakify experiments Cross-Validation To run the Flakify experiment using cross-validation on the two datasets, navigate to <code>src\</code> folder and run the following commands: <pre><code class="language-bash">bash Flakify_predictor_cross_validation.sh FlakeFlagger bash Flakify_predictor_cross_validation.sh IDoFT</code></pre> This will generate the classification results into <code>results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv</code> and <code>results/Flakify_cross_validation_results_on_IDoFT_dataset.csv</code> for the cross-validation experiments on both datasets. It will also save the weights of the two models trained on the FlakeFlagger and IDoFT datasets into <code>results/Flakify_model_trained_on_FlakeFlagger_dataset.pt</code> and <code>results/Flakify_model_trained_on_IDoFT_dataset.pt</code>, respectively.   Per-project Validation To run the Flakify experiment using per-project validation on the two datasets, navigate to <code>src\</code> folder and run the following commands: <pre><code class="language-bash">bash Flakify_predictor_per_project.sh FlakeFlagger bash Flakify_predictor_per_project.sh IDoFT</code></pre> This will generate the classification results into <code>results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv</code> and <code>results/Flakify_per_project_results_on_IDoFT_dataset.csv</code> for the whole per-project validation experiments on both datasets.   FlakeFlagger Replication This is the guideline for replicating the experiments we used to evaluate the two versions of FlakeFlagger, white-box and black-box, for classifying test cases as flaky and non-flaky using cross-validation on the FlakeFlagger dataset. Requirements: This is a list of all required python packages: <ul> <li>python =3.8.5</li> <li>imbalanced_learn= 0.8.1</li> <li>pandas= 1.3.3</li> <li>scikit_learn= 0.22.1</li> </ul> Input File: This is a list of input files that are required to accomplish this step: <ul> <li>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</li> <li>dataset/FlakeFlagger/FlakeFlaggerFeaturesTypes.csv</li> <li>dataset/FlakeFlagger/Information_gain_per_feature.csv</li> </ul> Output File: <ul> <li>results/FlakeFlagger_black-box_results.csv</li> <li>results/FlakeFlagger_white-box_results.csv</li> </ul>   Replicating FlakeFlagger experiments To run the FlakeFlagger experiments, navigate to <code>src\</code> folder and run the following command: <pre><code class="language-bash">bash FlakeFlagger_predictor.sh white-box bash FlakeFlagger_predictor.sh black-box</code></pre> This will generate the classification results into <code>results/FlakeFlagger_white-box_results.csv</code> and <code>results/FlakeFlagger_black-box_results.csv</code> for both white-box and black-box experiments, respectively.

opencc-by-4.0Aug 2022View details →

zenodo36/100

Results for Several Simple Algorithms on the W-Model for Black-Box Discrete Optimization Benchmarking

The W-Model follows a layered approach, where each layer can either be omitted or introduce a different characteristic feature such as neutrality via redundancy, ruggedness and deceptiveness, epistasis, and multi-objectivity, in a tunable way. The model problem is defined over bit string representations, which allows for extracting some of its layers and stacking them on top of existing problems that use this representation, such as OneMax, the Maximum Satisfisiability or the Set Covering tasks, and the NK landscape. The ruggedness and deceptiveness layer can be stacked on top of any problem with integer-valued objectives. Here we provide some experimental results with several simple algorithms on the W-Model along with the algorithm and experiment executor implementation. The most recent version of the code can be found at <a href="http://www.github.com/thomasWeise/BBDOB_W_Model">http://www.github.com/thomasWeise/BBDOB_W_Model</a>, while the program used in the experiment are attached to this dataset. The implemented algorithms are <ul> <li>1-flip hill climber with restarts,</li> <li>2-flip hill climber with restarts,</li> <li>μ+λ Evolutionary Algorithms (for different values of μ, λ, and different crossover rates),</li> <li>μ+λ Evolutionary Algorithms with Frequency Fitness Assignment (FFA) (for different values of μ, λ, and different crossover rates),</li> <li>exhaustive enumerate (EE), and</li> <li>random sampling (RS)</li> </ul> These algorithms are applied to a range of different parametric setups of the W-Model for single-objective optimization and fixed-length bit string representations. The experiments are executed on a HP Z640 Work Station with 32 GB DDR4-2400 RAM and Intel Xeon E5-2609v4 CPU under Ubuntu Server Linux 16.04, Kernel 4.4.0-116-generic, and with Java 1.8.0_151. Each run was granted at most 1048576 function evaluations (FEs). The total amount of data collected is about 45 GB, with tar.xz compression down to about 1.2 GB. The text files containing the algorithm traces are fairly self-describing. Publications that describe the W-Model: <ul> <li>Thomas Weise and Zijun Wu. Difficult Features of Combinatorial Optimization Problems and the Tunable W-Model Benchmark Problem for Simulating them. In Black Box Discrete Optimization Benchmarking (<a href="http://iao.hfuu.edu.cn/bbdob-gecco18">BB-DOB</a>) Workshop of Companion Material Proceedings of the Genetic and Evolutionary Computation Conference (<a href="http://gecco-2018.sigevo.org/">GECCO 2018</a>), July 15th-19th 2018, Kyoto, Japan, ISBN: 978-1-4503-5764-7. ACM. doi:<a href="http://dx.doi.org/10.1145/3205651.3208240">10.1145/3205651.3208240</a> / <a href="http://github.com/thomasWeise/BBDOB_W_Model">source codes</a> [This paper contains some errata for the one below]</li> <li>Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs. A Tunable Model for Multi-Objective, Epistatic, Rugged, and Neutral Fitness Landscapes. In Maarten Keijzer, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H. Holmes, Gregory S. Hornby, Daniel Howard, James Kennedy, Sanjeev P. Kumar, Fernando G. Lobo, Julian Francis Miller, Jason H. Moore, Frank Neumann, Martin Pelikan, Jordan B. Pollack, Kumara Sastry, Kenneth Owen Stanley, Adrian Stoica, El-Ghazali, and Ingo Wegener, editors, Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO'08), pages 795-802, July 12-16, 2008, Renaissance Atlanta Hotel Downtown: Atlanta, GA, USA. ISBN: 978-1-60558-130-9, New York, NY, USA: ACM Press. doi:<a href="http://dx.doi.org/10.1145/1389095.1389252">10.1145/1389095.1389252</a> / <a href="http://iao.hfuu.edu.cn/images/publications/WNSRG2008ATMFMOERANFL.pdf">pdf</a> [This paper has some deficits fixed in the paper above]</li> </ul>

opencc-by-4.0May 2018View details →

zenodo36/100

LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models - Replication Package

LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models This is the replication package associated with the paper "LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models". Replication Package Contents: This replication package contains all the necessary data and code required to reproduce the results reported in the paper. We provide the results of the Fault Detection Rate (FDR), Total Minimization Time (MT), Time Saving Rate (TSR) , statistical tests for all the minimization budgets (i.e., 25%, 50%, and 75%), results for the preliminary study, results for UniXcoder/Cosine with preprocessed code on 16 projects. Data: We provide in the Data directory the data used in our experiments, which is the source code of test cases (Java test methods) of 17 projects collected from Defects4J. Code: We provide in the Code directory the code (Python) and bash files required to run the experiments and reproduce the results. Results: We provide in the Results directory the detailed results for our approach (called LTM). We also provide the summarized results of LTM and a baseline (ATM) for comparison purposes. Additional technical details about ATM can be found at https://zenodo.org/record/7455766. _________________________________ LTM's Similarity Measurement: The source code of this step is in the Code/LTM/Similarity directory. Requirements: To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the Code/LTM/Similarity/requirements.txt file should be installed, as follows: cd Code/LTM/Similarity pip install -r requirements.txt Input: <ul> <li>Data/LTM/TestMethods</li> </ul> Output: <ul> <li>Data/LTM/similarity_measurements</li> </ul> Running the experiment: To measure the similarity between all pairs of test cases, the following bash script should be executed: bash measure_similarity.sh The source code of test methods of each project in the Data/LTM/TestMethods is parsed to generate pairs of test cases. This steps includes test methods tokenization, test methods embeddings extraction and similarity calculation. Then, all similarity scores are stored in Data/LTM/similarity_measurements folder. Due to the large size of the calculated similarity scores (60 GB), they were not uploaded on Zenodo, but they can be available upon request. LTM's Test Suite Minimization: The source code of this step is in the Code/LTM/Search directory. Requirements: To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the Code/LTM/Search/requirements.txt file should be installed, as follows: cd Code/LTM/Search pip install -r requirements.txt Input: Data/LTM/similarity_measurements Output: Results/LTM/minimization_results Running the experiments: To minimize the test suite for each project version, the following bash script should be executed: bash minimize.sh The similarity scores of all test case pairs per project version are parsed by the search algorithm (Genetic Algorithm). Each experiment runs ten times using three minimization budgets (25%, 50%, and 75%). The results are stored in the Results/LTM/minimization_results directory. LTM's Evaluation: To evaluate the minimization results for each version and each project, the following bash script should be executed: cd Code/LTM/Evaluation bash evaluate_per_version.sh cd Code/LTM/Evaluation bash evaluate_per_project.sh This will evaluate the FDR, MT and TSR results for each version and each project for each minimization budget. These results are stored in the Results/LTM directory. Note that for each version, the FDR is either 1 or 0. For each project, the FDR ranges from 0 to 1.

opencc-by-4.0Jul 2024View details →

zenodo36/100

GCN-TFiLM: Modelling Black-box Audio Effects with Time-varying Feature Modulation

Dataset, checkpoints, evaluation, and supplementary material associated with the following publication: <a href="https://arxiv.org/abs/2211.00497">Modelling Black-box Audio Effects with Time-varying Feature Modulation</a> Citation: <pre><code>@inproceedings{comunita2023modelling, title={Modelling black-box audio effects with time-varying feature modulation}, author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D}, booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} }</code></pre>

opencc-by-4.0Oct 2022View details →

zenodo32/100

Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization -- Reproducibility Files

This repository contains the files to reproduce the results from the paper "Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization". Data Collection In this folder, all files used to generate the raw performance data for the set of algorithms are included, as well as the code used to generate the ELA features. For the performance, the packages 'ioh', 'nevergrad', 'modde' and 'modcma' are essential, while 'pflacco' is used for ELA. For all scripts in this folder, the number of parallel threads and the folders for reading function settings (included as 3 csv files in this folder) and storing data should be set before execution.  Note that the full performance data exceeds 50GB, so it is not included in this repository. Instead, the results of processing it (using the aocc_extraction script) are included in the 'auc_MABBOB' folder (spread across multiple csv-files, with a version using a different budget factor included as well). The ELA data is included as 'ELA' and 'ELA_BBOB' for the affine combinations and component functions respectively.   Data Processing, Analysis and Visualization The remaining reproducibility files can be found in the Reproducibility folder. Within this folder are several notebooks which handle various steps in the pipeline, starting with preprocessing the data collected in the previous steps. This results in some csv-files, which are also included for convenience. Afterwards, the remaining notebooks deal with correlation analysis, instance selection methods, and all included plots from the paper. To match the environment used during our execution of these scripts. a yml-file (to be used with conda or mamba) is available as well.

opencc-by-4.0Apr 2024View details →

zenodo32/100

Deep Learning for Black-Box Modeling of Audio Effects

Accompanying audio samples for the paper: Martínez Ramírez M. A., Benetos, E. and Reiss J. D., “Deep Learning for Black-Box Modeling of Audio Effects” submitted to the Applied Sciences:  Acoustics and Vibration - Digital Audio Effects special issue, December, 2019. Dry and wet bass and guitar recordings. Dry notes are taken from the IDMT-SMT-Audio-Effects dataset. Author: Michael Stein (Fraunhofer IDMT) https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html Wet notes are recorded from the limiter and preamplifier of a Universal Audio 6176 Vintage Channel Strip and from the horn and woofer of a 145 Leslie speaker cabinet.

opencc-by-4.0Dec 2019View details →

zenodo32/100

Black-box Membership Inference Attacks against Fine-tuned Diffusion Models

We have provided some fine-tuned model checkpoints and datasets to help readers reproduce the experiments presented in the paper.

opencc-by-4.0Aug 2024View details →

zenodo16/100

Training Datasets of Exhaust Temperature Model (ETM) and Three-Way Catalyst Control Lambda Set Point (TWCC) Model for Black-Box Modeling

These are the two training datasets for the Exhaust Temperature Model (ETM) and the Three-Way Catalyst Control Lambda Set Point (TWCC) Model, used in Black-Box Modeling, such as through Neural Networks. The datasets for each black box model are stored in XLSX files. Each dataset file contains ECU channel inputs, related Map parameters, and output. The ECU channel inputs and Map parameters are collected with Sobol methods. The output is generated by Hardware-in-the-Loop (HiL) corresponding to the related ECU channel inputs and Map parameters. The ETM model has four effective Maps, hence there are 32 corresponding shape-based algorithm parameters. Its output is the exhaust temperature. Its ECU channel inputs are as follows: CH_1:       Intake valve cut-off condition CH_2:       Thrust cut-off condition CH_3:       Cylinder equalization control work cycle injection enabled CH_4:       Ignition angle efficiency CH_5:       Relative level of reduction CH_6:       Lean engine lambda for cylinders on exhaust bank with lambda split CH_7:       Rich engine lambda for cylinders on exhaust bank with lambda split CH_8:       Exhaust gas mass flow CH_9:       Engine speed CH_10:     Relative air filling CH_11:     Material temperature CH_12:     Engine temperature   The TWCC model has one Map, hence there are 8 corresponding shape-based algorithm parameters. Its output is lambda set point. Its ECU channel inputs are as follows: CH_1:       Exhaust mass flow CH_2:       Trigger bit for inhibiting the purge CH_3:       Trigger bit for a rich mixture CH_4:       Trigger bit for empty catalyst CH_5:       Trigger bit for catalyst reset CH_6:       Oxygen storage level CH_7:       Desired air-fuel ratio

restrictedcc-by-4.0Apr 2024View details →

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