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478 results for “artifact”
Data for Mathematical Expressions in Software Engineering Artifacts
<p>Data for the experiments in the paper, Mathematical Expressions in Software Engineering Artifacts.</p> <p>The dataset contains the following sub-directories:</p> <ol> <li>Bug_data: Bug reports from 10 open-sourced projects.</li> <li>MathyB_A: Bug reports annotated by MEDSEA.</li> <li>MathyB_H: Bug reports annotated manually by humans.</li> <li>NNGen_modified_data: Modified log messages of <a href="https://github.com/Tbabm/nngen">NNGen</a> dataset (<a href="https://dl.acm.org/doi/10.1145/3238147.3238190">Neural-machine-translation-based commit message generation: how far are we?</a>) based on the annotations made by MEDSEA.</li> </ol>
Artifacts for [On Rank Aggregating Test Prioritizations]
<p>Artifacts for [On Rank Aggregating Test Prioritizations]<br> =====================================================</p> <pre><code>username: entp password: entp root password: entp</code></pre> <p>Directory structure for [/home/entp/<strong>EnTP</strong>]<br> -----------------------------------------------------------------<br> [<strong>benchmarks</strong>] -> contains the projects/subjects under test used in our study.</p> <p>[<strong>cost_cov_diff</strong>] -> pre-recorded cost (cachegrind's I-Ref count) per test-case. We recorded this precporcessing to avoid fluctuations in costs across different systems, and hence maintain uniformity.</p> <p>[<strong>raw_data_scripts</strong>] -> contains C++ implementation of EnTP, and scripts to generate results (tables, boxplots, .csv, .txt, etc.)</p> <p>[<strong>exp_res_raw</strong>] -> contains pre-recorded experiments results (as .csv files) as reported in the paper. To generate the plots, run [python3 plot.py] from {/home/entp/EnTP/exp_res_raw}. The plots will be generated in .eps format. These precomputed results helps the artifact reviewer save time by generating the plots only, otherwise the whole process (detailed in Makefile's usage) generates a [database] directory of ~400GB (all benchmarks, all combinations, all results even beyond those reported in the paper, + some extra logs) which is very time consuming (~1 month on a standard laptop with 8 cores, 8GB RAM).</p> <p><strong>Makefile</strong>'s usage<br> ---------------------------<br> [Step 1]</p> <pre><code class="language-bash">entp@entp:~/EnTP$ make -s entp_all_[benchmark]</code></pre> <p>Possible values of [benchmark] = {c4, gravity, mlisp, replace, schedule2, space, xc, cf, grep, printtokens, scd, sed, tcas, xxhash, flex, gzip, printtokens2, schedule, slre, totinfo}.</p> <p>example:</p> <pre><code class="language-bash">entp@entp:~/EnTP$ make -s entp_all_slre</code></pre> <p>executes EnTP and state-of-the-arts on the benchmark "slre". Please follow log messages displayed after executing the above command. At the end of the execution, a sub-diretory named [<strong>database</strong>] will store the results for all experiments performed on "slre" for the current system and environment.<br> ...<br> [<em>database/{100, 75, 50, 25}</em>] -> contains experimental results for the consensus budget of top-{100%, 75%, 50%, 25%}.<br> ...</p> <p>(optional)</p> <pre><code class="language-bash">entp@entp:~/EnTP$ make -s entp_all_[benchmark] #other benchmarks</code></pre> <p>[Step 2]</p> <pre><code class="language-bash">entp@entp:~/EnTP$ make -s generate_tabs</code></pre> <p>This will collect data from the newly generated directories and results (at the end of previous step), and generate tables, and .eps plots.</p> <p>Check for results reproduced<br> ---------------------------------------------<br> You can visually compare the .eps plots in {<strong><em>/home/entp/EnTP</em></strong>} with the ones generated in the directory {<strong><em>/home/entp/EnTP/exp_res_raw</em></strong>}.<br> You can also compare the .csv files under these directories for quantitative comparison with some tolerance.</p> <p>(optional)</p> <pre><code class="language-bash">entp@entp:~/EnTP$ make -s destroy_all</code></pre> <p>Cleans up everything!</p>
FIGURE 1 in Fourier transform infrared spectroscopy (FTIR) characteristics of ancient amber artifacts of the Han Dynasty from Hunan, China
FIGURE 1. Possible ancient trade roadmap of both the Eurasian Steppe Silk Road and the Maritime Silk Road. 1, the present Capital, Beijing; 2, the ancient Capital city during the Han Dynasty, Xi'an, Shaanxi Province; 3, Nanyang, Henan Province, where unearthed artifacts of Han Dynasty made by Burmese amber (Chen et al., 2019); 4, Tomb Changchen M010, Changsha, Hunan Province; 5, Tomb Chentie M001, Chenzhou, Hunan Province.
FIGURE 3 in Fourier transform infrared spectroscopy (FTIR) characteristics of ancient amber artifacts of the Han Dynasty from Hunan, China
FIGURE 3. FTIR spectra of amber samples. A, SYS20220716-1. B, SYS20020716-2. C, SYS20220716-4. D, SYS20220716-5.
Artifact For A Large Scale Analysis of Semantic Versioning in NPM
<p>This is the artifact for: A Large Scale Analysis of Semantic Versioning in NPM.</p> <p>The artifact contains:</p> <ul> <li>A full scrape of all metadata from NPM (package / version information, dependencies, etc.) as of October 31, 2022.</li> <li>A copy of our code, which includes the software for scraping metadata and package tarball (code) data, as well as all analysis scripts that are needed to replicate the figures from the paper.</li> </ul>
Artifacts for paper "SAGA_Summarization-Guided Assert Statement Generation" submitted to JCST
<p>The project includes the source codes, datasets and experimental results used in the submitted JCST paper titled "SAGA: Summarization-Guided Assert Statement Generation"</p>
Artifact Evaluation for "ARMore: Pushing Love Back Into Binaries"
<p>Artifact Evaluation for "ARMore: Pushing Love Back Into Binaries".</p> <p>Please see the relevant Artifact Appendix pdf from Usenix for instructions on how to run it.</p>
The Artifacts of OOPSLA-2023 Submission #197
<p>This is the online repository of the OOPSLA-2023 Submission #197. We have released the source code and dataset.</p> <ul> <li><strong>Dataset</strong>: Our evaluation is based on the large-scale CodeSearchNet dataset. Use the following command to download and preprocess the data:</li> </ul> <pre><code class="language-bash">cd dataset bash run.sh cd ..</code></pre> <ul> <li> <p><strong>Dependencies</strong></p> </li> </ul> <pre><code class="language-bash">pip install -r requirements.txt</code></pre> <p> [Optional] We have built the tree-sitter parser stored at `evaluator/CodeBLEU/parser/languages.so`. If it doesn't work for you, it can be rebuilt with the following command:</p> <pre><code class="language-bash">cd evaluator/CodeBLEU/parser bash build.sh</code></pre> <ul> <li> <p><strong>Training</strong></p> </li> </ul> <pre><code class="language-bash">bash sh/train.sh [python/java] [CodeT5/Natgen] [CodeBERT/GraphCodeBERT]</code></pre> <ul> <li> <p><strong>Evaluation</strong></p> </li> </ul> <p> Evaluate generator:</p> <pre><code class="language-bash">bash sh/evaluate.sh [python/java] [CodeT5/Natgen]</code></pre> <p> Evaluate discriminator:</p> <p> We evaluate the discriminator by reusing the code from <a href="https://github.com/microsoft/CodeBERT/tree/master/CodeBERT/codesearch">CodeBERT</a> and <a href="https://github.com/microsoft/CodeBERT/tree/master/GraphCodeBERT/codesearch">GraphCodeBERT</a>. According to the Evaluate section in the corresponding model's Readme, replace `model_name_or_path` with the discriminator that you want to evaluate.</p> <p> </p>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This artifact contains:</p> <p>1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md).</p> <p>2. All classes used in the experiments [./classes.csv](./classes.csv).</p> <p>3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).</p> <p> </p>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This zip file is the artifact that contains the following:</p> <p>1. The binary. </p> <p>2. All classes used in the experiments.</p> <p>3. The experimental data.</p> <p> </p>
Artifacts for the ISSTA 2023 Paper: An Empirical Study on the Effects of Obfuscation on Static Machine Learning-based Malicious JavaScript Detectors
<p>An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors</p> <p>This repository contains the evaluation script and the corresponding data of the ISSTA'23 paper "An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors".</p> <p>Abstract</p> <p>Machine learning is increasingly being applied to malicious JavaScript detection in response to the growing number of Web attacks and the attendant costly manual identification. In practice, to hide their malicious behaviors or protect intellectual copyrights, both malicious and benign scripts tend to obfuscate their own code before uploading. While obfuscation is beneficial, it also introduces some additional code features (e.g., dead code) into the code. When machine learning is employed to learn a malicious JavaScript detector, these additional features can affect the model to make it less effective. However, there is still a lack of clear understanding of how robust existing machine learning-based detectors are on different obfuscators.</p> <p>In this paper, we conduct the first empirical study to figure out how obfuscation affects machine learning detectors based on static features. Through the results, we observe several findings: 1) Obfuscation has a significant impact on the effectiveness of detectors, causing an increase both in false negative rate (FNR) and false positive rate (FPR), and the bias of obfuscation in the training set induces detectors to detect obfuscation rather than malicious behaviors. 2) The common measures such as improving the quality of the training set by adding relevant obfuscated samples and leveraging state-of-the-art deep learning models can not work well. 3) The root cause of obfuscation effects on these detectors is that feature spaces they use can only reflect shallow differences in code, not about the nature of benign and malicious, which can be easily affected by the differences brought by obfuscation. 4) Obfuscation has a similar effect on realistic detectors in VirusTotal, indicating<br>that this is a common real-world problem.</p> <p>Getting Started</p> <p>Requirements</p> <pre>install python3 version 3.9.12 pip3 install -r requirements.txt install nodejs install npm npm install escodegen npm install esprima</pre> <p>Step 1: Generating PDGs for JStap</p> <p><code>cd detectors/jstap/pdg_generation</code></p> <p><code>python generate_PDGs.py</code></p> <p>Step 2: Getting the results for RQ1: What Impact Does Obfuscation Have on Static Machine Learning Malicious JavaScript Detectors?</p> <p><code>cd RQ1/</code></p> <p>1. Detectors Performance on Obfuscated Samples.</p> <p>To train the models:</p> <p><code>python RQ1_1_train.py</code></p> <p>To get the results:</p> <p><code>python RQ1_1_test.py</code></p> <p>2. Different Machine Learning Algorithms.</p> <p>To train the models:</p> <p><code>python RQ1_2_train.py</code></p> <p>To get the results:</p> <p><code>python RQ1_2_test.py</code></p> <p>3. Biased Training Sets</p> <p>To train the models:</p> <p><code>python RQ1_3_train.py</code></p> <p>To get the results:</p> <p><code>python RQ1_3_test.py</code></p> <p>All the trained models will be stored in RQ1/models/.</p> <p>All the results will be stored in RQ2/results/.</p> <p>Step 3: Getting the results for RQ2: Are the Common Measures to Mitigate the Impact of Obfuscation Effective?</p> <p><code>cd RQ2/</code></p> <p>1. Training and Testing Detectors on Samples with Same Types of Obfuscation.</p> <p>To train the models:</p> <p><code>python RQ2_1_train.py</code></p> <p>To get the results:</p> <p><code>python RQ2_1_test.py</code></p> <p>2. Training and Testing Detectors on Samples with Different Types of Obfuscation.</p> <p>If you follow the steps, the models is already trained.</p> <p>To get the results:</p> <p><code>python RQ2_2_test.py</code></p> <p>3. BERT Variants.</p> <p>To get the results:</p> <p><code>python RQ2_3.py</code></p> <p>All the trained models will be stored in RQ2/models/.</p> <p>All the results will be stored in RQ2/results/.</p> <p> </p> <p>Step 4: Getting the results for RQ3: What Is the Root Cause of Obfuscation Affecting Static Machine Learning Malicious JavaScript Detectors?</p> <p>To get the results of vectors visualization, top ten features, and distances between vectors sets:</p> <p><code>cd RQ3</code></p> <p><code>python visulization.py</code></p> <p>The figures of vectors visualization will be stored in RQ3/results/.</p> <p>Other results will be shown in the console.</p> <p> </p> <p>Step 5: Getting the results for RQ4: How Does Obfuscation Affect Real-world Static Malicious JavaScript Detectors?</p> <p>To get the results, submit the sample under the folder samples/ to <a href="https://www.virustotal.com/gui/home/upload">VirusTotal</a> .</p> <p> </p> <p>Detailed Instructions</p> <p>detectors</p> <p>The detectors under the folder <code>detectors/</code> are the main projects to be evaluated in our paper, which are <strong>CUJO</strong>, <strong>ZOZZLE</strong>, <strong>JAST</strong>, and <strong>JSTAP</strong>.</p> <p>Detailed setup and usage instructions are described in <code>README.md</code> in the corresponding folder.</p> <p>samples</p> <p>The files under the folder <code>samples/</code> are the samples from a random tenth of our dataset used in our paper.</p> <p>Results can be obtained quickly using these samples. These results will not be exactly the same as in the paper, but they are similar.</p> <p>RQ1</p> <p>The code under folder <code>RQ1/</code> is to figure out how obfuscation affects these detectors.</p> <p><code>RQ1_1_train.py</code> is to train four detectors with unobfuscated samples.</p> <p><code>RQ1_1_test.py</code> tests these trained detectors with unobfuscated and obfuscated samples.</p> <p><code>RQ1_2_train.py</code> is to train the detector <strong>ZOZZLE</strong> that uses different machine learning algorithms.</p> <p><code>RQ1_2_test.py</code> tests these trained models with unobfuscated and obfuscated samples.</p> <p><code>RQ1_3_train.py</code> uses a training set with all unobfuscated benign samples and all obfuscated malicious samples, and a training set with all obfuscated benign samples and all unobfuscated malicious samples to train the detectors.</p> <p><code>RQ1_3_test.py</code> uses these detectors to detect unobfuscated benign samples, obfuscated benign samples, unobfuscated malicious samples, and obfuscated malicious samples, respectively.</p> <p>RQ2</p> <p>The code under folder <code>RQ2/</code> is to study the two measures to mitigate the impact of obfuscation effective or not.</p> <p><code>RQ2_1_train.py</code> uses obfuscated samples to train four detectors.</p> <p><code>RQ2_1_test.py</code> tests these detectors on the same type of obfuscated samples.</p> <p><code>RQ2_2_test.py</code> tests thest detectors on the different type of obfuscated samples.</p> <p><code>RQ2_3.py</code> uses the BERT variants to generate code representation of unobfuscated samples, trains the detector with these code representations, and tests the trained detectors with code representations of obfuscated samples.</p> <p>RQ3</p> <p>The code unser fodler <code>RQ3/</code> visualizes the vectors, extracts the ten most important features, and calculates the distance between different sets of vectors.</p> <p>RQ4</p> <p>There is no code related to RQ4 here because the actual operation of RQ4 is to submit the samples to <a href="https://www.virustotal.com/gui/home/upload">VirusTotal</a> .</p> <p> </p> <p>The whole dataset is available at <a href="https://drive.google.com/file/d/1a7pNUwzikiJyY9L7dIu53I6_MR0oDpgi/view?usp=sharing." target="_blank" rel="noopener">https://drive.google.com/file/d/1a7pNUwzikiJyY9L7dIu53I6_MR0oDpgi/view?usp=sharing.</a></p> <p> </p> <p>Cite this work</p> <pre>@inproceedings{staticanalysis, author = {Kunlun Ren, Qiang Weizhong, Yueming Wu, Yi Zhou, Deqing Zou, Hai Jin}, title = {An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors}, booktitle = {Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'23)}, year = {2023} }</pre> <p> </p>
Artifacts for "FLAG: Finding Line Anomalies (in code) with Generative AI"
<p>Artifacts for our work used to detect defects in code using LLM consistency checking. Please read README.md file in repository to start.</p>
MOON: Assisting Students in Completing Educational Notebook Scenarios (Artifacts)
<p>This repository contains the artifacts supporting the paper "<em>MOON: Assisting Students in Completing Educational Notebook Scenarios</em>" published in the <em>IEEE Symposium on Visual Languages and Human-Centric Computing</em> (VL/HCC) 2023.</p> <ul> <li><strong>moon.zip</strong> contains the source code of MOON at the time of publication</li> <li><strong>notebooks.zip</strong> contains the example Jupyter notebook used as illustration in the paper as well as the notebook manipulated by students in the evaluation</li> <li><strong>analysis.zip</strong> contains the Jupyter notebooks used to analyze the raw data extracted in the evaluation as well as the results of the user study conducted with students</li> </ul>
Artifact of Enhancing Search-Based Unit Test Generation with Method-Level Java Class Splitter
<p># Artifact of Enhancing Search-Based Unit Test Generation with Method-Level Java Class Splitter</p> <p> </p> <p>This artifact contains:</p> <p>1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md).</p> <p>2. All classes used in the experiments [./classes.csv](./classes.csv).</p> <p>3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).</p>
Artifact for paper "An Experimental Evaluation of Conformance Testing Techniques in Active Automata Learning" at MODELS 2023
<p>This is the archival artifact intended for artefact evaluation of the paper "An Experimental Evaluation of Conformance Testing Techniques in Active Automata Learning" accepted at MODELS 2023. </p>
FEDORA. Artifacts from two study groups on inter/multi/transdisciplinary forms of knowledge organization and multi-actor contexts of knowledge construction
<table> <tbody> <tr> <td> <p>The dataset contains artifacts from two intensive multi-actors and inter-disciplinary study groups realized within the FEDORA project. The basic artifacts are jamboard used to collect and share experiences of interdisciplianity in terms of: barriers, features of good interdisciplinarity experiences, interdisciplinary skills, forms of knowledge organization.</p> <p>The study groups involved 14 participants with diverse backgrounds (physics, chemistry, educational science, management, mathematics, philosophy, history of science, sociology, science communication). The interdisciplinary group discussions drew on the reading selected by UNIBO and experience from international projects and yielded three narratives of the barriers, conditions that facilitate interdisciplinarity, and interdisciplinary attitudes and skills to inter/multi/transdisciplinarity. The results are detailed in the deliverable D1.1 (DOI 10.5281/zenodo.7519006)</p> </td> </tr> </tbody> </table>
KPP-3 Artifact for WarpX <> Clover-SLATE: RZ Simulation on Frontier
<p>This documents a run of WarpX in the quasi-cylindrical "RZ" geometry with the Pseudo Spectral Analytical Time Domain (PSATD) field solver. The simulation was run on Frontier (OLCF).</p> <p>ECP CLOVER project SLATE provides WarpX with the GPU capable C++ libraries BLAS++ and LAPACK++, which are used for geometric transfer matrix calculations in RZ. Furthermore, using BLAS++, manually-written, node-local linear algebra operations were replaced for significant speedups, e.g., up to 5x faster on MI250X for a Hankel transformation.</p> <p>WarpX documentation: https://warpx.readthedocs.io</p>
NDPI Bio-Formats artifact examples
<p>Images hosted in the <a href="https://images.jax.org/webclient/?show=project-1301">Gates Foundation</a> project at images.jax.org.</p> <p>Copied here to facilitate troubleshooting striping artifact with Bio-Formats. </p> <p> </p>
Verifying Performance Guidelines for MPI Collectives at Scale - PMBS@SC23 Artifact
<p>ARTIFACT DESCRIPTION/ARTIFACT EVALUATION</p> <p> </p>
Artifacts for "FLAG: Finding Line Anomalies (in code) with Generative AI"
<p>Artifacts for our work used to detect defects in code using Large Language Models. Please read README.md file in repository to start.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.