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708 results for “Java”
Technical Leverage Dataset for Java Dependencies in Maven
<p>In finance, leverage is the ratio between assets borrowed from others and one's own assets. A matching situation is present in software: by using free open-source software (FOSS) libraries a developer leverages on other people's code to multiply the offered functionalities with a much smaller own codebase. In finance as in software, leverage magnifies profits when returns from borrowing exceed costs of integration, but it may also magnify losses, in particular in the presence of security vulnerabilities. We aim to understand the level of technical leverage in the FOSS ecosystem and whether it can be a potential source of security vulnerabilities. Also, we introduce two metrics change distance and change direction to capture the amount and the evolution of the dependency on third-party libraries. Our analysis published in [1] shows that small and medium libraries (less than 100KLoC) have disproportionately more leverage on FOSS dependencies in comparison to large libraries. We show that leverage pays off as leveraged libraries only add a 4% delay in the time interval between library releases while providing four times more code than their own. However, libraries with such leverage (i.e., 75% of libraries in our sample) also have 1.6 higher odds of being vulnerable in comparison to the libraries with lower leverage.</p> <p>This dataset is the original dataset used in the publication [1]. It includes 8494 distinct library versions from the FOSS Maven-based Java libraries An online demo for computing the proposed metrics for real-world software libraries is also available under the following URL: https://techleverage.eu/.</p> <p>The original publication is [1]. An executive summary of the results is avialble as the publication [2]. This work has been funded by the European Union with the project AssureMOSS (https://www.assuremoss.eu).</p> <p>[1] Massacci, F., & Pashchenko, I. (2021, May). Technical leverage in a software ecosystem: Development opportunities and security risks. In <em>2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)</em> (pp. 1386-1397). IEEE.</p> <p>[2] Massacci, F., & Pashchenko, I. (2021). Technical Leverage: Dependencies Are a Mixed Blessing. <em>IEEE Secur. Priv.</em>, <em>19</em>(3), 58-62.</p>
Accepted Artifact for Profiling and Optimizing Java Streams
<p>The accepted artifact for the journal article "Profiling and Optimizing Java Streams" published in Volume 7, Issue 3 of <a href="https://programming-journal.org/"><em>The Art, Science, and Engineering of Programming</em></a>.</p> <p>Please refer to the README.md to work with the artifact and reproduce results from the article.</p>
Preprocessed Java Code Corpus
<p>A preprocessed code corpus for the Java programming language.<br> The corpus was used for the experiments in the paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.<br> It contains preprocessed-tokenized files for training, validation, testing, and BPE encoding learning.<br> The BPE segmented versions of the above files are also included for three different encoding sizes i,e., 2000, 5000, and 10000 BPE merge operations as well as the learned BPE encodings.<br> Similar versions are also contained for splitting compound identifiers on camelCase and snake_case as in (Allamanis et al., 2015) as well as the corresponding subtoken maps.</p>
GitHub Java Corpus - Function Identifiers
<p>This dataset contains function identifiers extracted from the GitHub Java Corpus (<a href="http://groups.inf.ed.ac.uk/cup/javaGithub/">http://groups.inf.ed.ac.uk/cup/javaGithub/</a>).</p> <p>Each line corresponds to a method declaration. A line contains the name of the method declaration followed by the function identifiers (i.e., function calls) contained within the method body. </p> <p>The file embeddings_train.json can be used to train a word/sentence embedding model using the code in the Github repository (link below).</p> <p>The corpus was used for the experiments in the paper <strong>Combining Code Embedding with Static Analysis for Function-Call Completion</strong>.</p> <p>Github repository to replicate the experiments: https://github.com/mweyssow/cse-saner</p>
Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects
<p>The dataset contains analyzed projects and modules for the paper "Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects". The contents are the following:</p> <ul> <li><a href="/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/manual-studied-modules.csv?versionId=8258b59c-3f88-487b-a4a3-007cb362a44a">manual-studied-modules.csv</a>: Manually analyzed Maven modules mentioned in Section 5.2</li> <li><a href="https://zenodo.org/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/projects.zip">projects.zip</a>: Instrumented and Mutated Github Projects. Projects list applied mutation changes, and their dynamic and static call graph.</li> </ul>
PPHPC Java vs OpenCL-CPU Datasets
<p><strong>Overview</strong></p> <p>These datasets contain the results from a performance comparison between Java and OpenCL/CPU implementations of the PPHPC model.</p> <p>The datasets are used in the following study:</p> <p>Fachada, N. and Rosa, A.C., Assessing the feasibility of OpenCL CPU implementations for agent-based simulations. <em>IWOCL 2017: Proceedings of the 5th International Workshop on OpenCL</em>, Article No. 4, 2017, https://doi.org/10.1145/3078155.3078174</p> <p><strong>Experiment reproducibility</strong></p> <p>The datasets were generated with the java.sh and cpu_l1.sh scripts under the following versions of the supporting software:</p> <ul> <li>cf4ocl v2.1.0</li> <li>cl_ops v0.2.0</li> <li>PPHPC, branch opencl, commit c17705882418 (release build, profiling off)</li> <li>OpenCL: <ul> <li>Intel (CPU): OpenCL Runtime 16.1.1 for Intel(R) Core(TM) and Intel(R) Xeon(TM) Processors for Ubuntu (64-bit) reported driver version: 1.2.0.25)</li> <li>AMD (CPU): APP SDK 3.0 for 64-bit Linux (reported driver version: 1800.8)</li> </ul> </li> </ul> <p>Hardware and operating system:</p> <ul> <li>Intel Xeon CPU E5-2650 v3 @ 2.30GHz (ten cores, two logical processors per core), 64GB RAM</li> <li>Ubuntu 16.04.1 LTS</li> </ul> <p><strong>Licenses</strong></p> <p>The datasets are made available under a CC-BY 4.0 license (see LICENSE_DATA.txt).</p> <p>The shell scripts and performance/statistical analysis scripts are made available under the MIT license (see LICENSE_SCRIPTS.txt).</p>
Data for the Manuscripts of "Variability of Jakarta Rain-Rate Characteristics Associated with the Madden-Julian Oscillation and Topography" and "Subdaily Rain-Rate Properties in Western Java Analyzed Using C-Band Doppler Radar"
<p>This archive consists of the post-processed data of C-Band Doppler Radar (CDR) over Jakarta and surrounding regions for the studies of "Variability of Jakarta Rain-Rate Characteristics Associated with the Madden-Julian Oscillation and Topography" and "Subdaily Rain-Rate Properties in Western Java Analyzed Using C-Band Doppler Radar".</p> <p>The dataset is a gridded rainfall data derived from the local relationship of Z (reflectivity) from the CDR and rainfall (R) from stations. The derived rainfall data are in daily estimates from 2009 to 2012 with the format in NetCDF files.</p> <p>The CDR data were obtained from the projects “Hydrometeorological Array for Intraseasonal Variation-Monsoon Automonitoring (HARIMAU)” (JFY 2005-2009), and the Science Technology Research Partnership for Sustainable Development (SATREPS) “Maritime Continent Center of Excellence (MCCOE) (JFY 2009-2013) of the Japan Science and Technology Agency (JST)/Japan International Cooperation Agency(JICA) under a collaboration of the Agency for the Assessment and Application of Technology (BPPT)-Indonesia and Japan Agency for Marine-earth Science and Technology (JAMSTEC)-Japan.</p>
Taxonomy of Security Weaknesses in Java and Kotlin Android Apps
<p>This is the replication package for the paper "Taxonomy of security weaknesses in Java and Kotlin Android apps" accepted for inclusion in The Journal of Systems & Software</p>
Water Body Checklists 2019: Java Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Java Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Java Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Java Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
The GNSS time series along the northern coastline of Java, Indonesia
<p>This repository contains the GNSS time series along the northern coastline of Java, both in .rneu and the GAGE's .pos format (https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf). The repository also contains the stations' coordinates. </p> <p>Notes:<br> In the .rneu format of the GNSS time series:<br> 1. The outliers have been removed.<br> 2. The offsets due to instrument changes at CGON in mid-2016 and at CSIT in late 2015 have been corrected.</p> <p>Please refer to:<br> Susilo, S., Salman, R., Hermawan, W. <em>et al.</em> GNSS land subsidence observations along the northern coastline of Java, Indonesia. <em>Sci Data</em> <strong>10</strong>, 421 (2023). https://doi.org/10.1038/s41597-023-02274-0</p>
Dataset for the study of Potential Code Borrowing and License Violations in Java Projects on GitHub
<p>This is the dataset for the study of Potential Code Borrowing and License Violations in Java Projects on GitHub. The dataset is based on the Public Git Archive and consists of projects on GitHub that have at least 50 stars and have at least one line in Java. A total of 23,378 projects are listed here that we downloaded for analysis on June 1st, 2019.</p>
List of licenses discovered in the study of Potential Code Borrowing and License Violations in Java Projects on GitHub
<p>This is the list of licenses discovered in the study of Potential Code Borrowing and License Violations in Java Projects on GitHub. The licenses are ranged by the amount of files that they cover, there are a total of 94 different licenses. Where possible, the names are presented as identifiers at https://spdx.org/licenses/. "GitHub" stands for no license in the file or the project.</p>
OW2 Decoder java use-cases data (WP6)
<p><strong>OW2 Decoder WP6 data</strong></p> <p>OW2 data for use-cases Authzforce, Joram, Lutece, Sat4j.<br> See README in related directories.</p> <p>All these data have been extracted from the Gitlab repository<br> at https://gitlab.ow2.org/decoder/decoder .</p> <p><em># Update Dec. 15, 2020</em></p> <p>As of M24, WP6 data include all data required by scientific work packages.<br> They have notably been used for WP1 and WP2 deliverables (the java statistics), and are under integration with the PKM (the JML part).</p> <p>Some JML data go beyond OW2 use-cases, with a MyThaiStar dataset<br> (Cap Gemini use-case) added to the collection.</p> <p> </p> <p> </p>
A Clustering-Based Approach for Visual Debugging of Concurrent Java Programs
<p>Supplementary Material to: A Clustering-Based Approach for Visual Debugging of Concurrent Java Programs</p>
Figure 2 in First record of the Small-eyed loter Prionobutis microps (Weber, 1907) (Teleostei: Eleotridae: Butinae) in Java, Indonesia
Figure 2. – Map of the known distribution of Prionobutis microps. New record in Java, Indonesia (black star); published records (black circles) are based on Larson (2021).
Figure 1 in First record of the Small-eyed loter Prionobutis microps (Weber, 1907) (Teleostei: Eleotridae: Butinae) in Java, Indonesia
Figure 1. – Collected specimens of Prionobutis microps, EFRM 0003, 19.1 cm TL (A), EFRM 0039, 22.5 cm TL (B), Solo River Estuary, East Java, Indonesia.
Figure 3 in First record of the Small-eyed loter Prionobutis microps (Weber, 1907) (Teleostei: Eleotridae: Butinae) in Java, Indonesia
Figure 3. – Solo River Estuary, East Java Province, Indonesia. Collection site where Prionobutis microps was found in Java, Indonesia.
Figs 26-31 in On linyphiid spiders from Java, Indonesia, with the description of three new genera and four new species (Araneae: Linyphiidae)
Figs 26-31. Details of male palp structure of Parameioneta javaensis sp. nov. (26) Right palp, retrolateral view. (27) Paracymbium, lateral view. (28) Palpal tibia, dorsal view. (29) Embolic division, ventro-lateral view. (30) Lamella characteristica, ventrolateral view. (31) Embolus, ventro-lateral view.
Figs 1-9 in On linyphiid spiders from Java, Indonesia, with the description of three new genera and four new species (Araneae: Linyphiidae)
Figs 1-9. Photographs of males: the holotype of Javagone maribaya sp. nov. (1-4), the paratype of Javanaria gracilipes sp. nov. (5-6) and the holotype (7) and both paratypes (8-9) of Javanyphia gede sp. nov. (1, 5, 8-9) Habitus, dorsal view. (2, 6-7) Prosoma, lateral view. (3) Prosoma, frontal view. (4). Prosoma, dorsal view.
ScienceDex guides
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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.