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260 results for “Empirical Study”
An Empirical Study on the Survival Rate of GitHub Projects
<p>This zip provides the replication package for the paper titled <em>An Empirical Study on the Survival Rate of GitHub Projects</em> sent to the <em>Mining Software Repositories 2022</em> conference, currently under review.</p>
Dataset for "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs"
<p>This package provides all published resources used and produced in the context of the research study leading to the article <em>"Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs"</em>, presented in ICSA 2022's technical track.</p>
Research Artefact: An Empirical Study of React-Library Related Issues via Stack Overflow
<p>This research artifact accompanies the paper titled "An Empirical Study of React-Library Related Issues via Stack Overflow." It is a comprehensive repository that includes the collected dataset containing 447,542 React-related Stack Overflow question posts, as well as 384 representative samples obtained randomly. The primary objective of this artifact is to facilitate the replication of our dataset for researchers and allow them to utilize it for further investigations and research purposes.</p>
Dataset for An Empirical Study of Hackathon Code Creation and Reuse
<p>This dataset is corresponds to our extended analysis done for “The Secret Life of Hackathon Code Where does it come from and where does it go?” (<a href="https://doi.org/10.1109/MSR52588.2021.00020">https://doi.org/10.1109/MSR52588.2021.00020</a>, pre-print at: <a href="https://arxiv.org/abs/2103.01145">https://arxiv.org/abs/2103.01145</a>) and “Tracking Hackathon Code Creation and Reuse” (<a href="https://doi.org/10.1109/MSR52588.2021.00085">https://doi.org/10.1109/MSR52588.2021.00085</a>, pre-print at: <a href="https://arxiv.org/pdf/2103.10167">https://arxiv.org/pdf/2103.10167</a>). The replication package including the scripts used for generating this dataset from the “World of Code” (https://worldofcode.org/) dataset is available in Github link <a href="https://github.com/woc-hack/track_hack">https://github.com/woc-hack/track_hack</a>.</p> <p>The dataset contains the blob hashes used in the scope of the analysis and the analysis outcome.</p> <p>The columns are as following</p> <ul> <li><strong>DevpostID</strong>: Devpost identification for the hackathon project and it can be used to get the URL for the devpost.com website. Example DevpostID -q9nd5 can be translated to https://devpost.com/software/-q9nd5</li> <li><strong>ProjectID</strong>: The Github project name</li> <li><strong>HackathonEndDate</strong>: Hackathon event end date</li> <li><strong>BlobHash</strong>: The blob hash used in the analysis</li> <li><strong>BeforeHackathon-DuringHackathon-AfterHackathon</strong>: This column represents if the blob was first introduced before/during/after the hackathon (1: before, 2: during, 3: after)</li> <li><strong>SameAuthor-Contributor-OtherAuthor</strong>: This column represents if the blob was first created by someone in the hackathon team, or someone who was a contributor to a project in which one of the members of the hackathon project contributed to as well (contributor), or someone else outside of the hackathon team (1: Author is a hackathon team member, 2: Author Contributed before with a hackathon team member, 3: Author is not related to the hackathon team).</li> <li><strong>UsedBySmallProject-UsedByMediumProject-UsedByLargeProject</strong>: This column represents if the hackathon blob is reused again after the hackathon event and what is the project size that reused the code (1: not reused, 3: reused in small project, 4: reused in medium project, 5: reused in large project, NA: not in scope of the reuse analysis)</li> <li><strong>IsTemplate</strong>: This column represents if the blob is identified as a template file or not ( YES: File is template, NO: File is not template). The template definition and method is explained in the research paper.</li> </ul>
Data for empirical example in: An effect size for comparing the strength of morphological integration across studies
<p>Understanding how and why phenotypic traits covary is a major interest in evolutionary biology. Biologists have long sought to characterize the extent of morphological integration in organisms, but comparing levels of integration for a set of traits across taxa has been hampered by the lack of a reliable summary measure and testing procedure. Here we propose a standardized effect size for this purpose, calculated from the relative eigenvalue variance, Vrel. First we evaluate several eigenvalue dispersion indices under various conditions, and show that only Vrel remains stable across samples size and the number of variables. We then demonstrate that Vrel accurately characterizes input patterns of covariation, so long as redundant dimensions are excluded from the calculations. However, we also show that the variance of the sampling distribution of Vrel depends on input levels of trait covariation, making Vrel unsuitable for direct comparisons. As a solution, we propose transforming Vrel to a standardized effect size (Z-score) for representing the magnitude of integration for a set of traits. We also propose a two-sample test for comparing the strength of integration between taxa, and show that this test displays appropriate statistical properties. We provide software for implementing the procedure, and an empirical example illustrates its use.</p>
A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store
<p>This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the <a href="https://github.com/mooselab/Sports-Apps-Analysis">replication package</a> to direct you to help replicate it for your dataset too. </p> <p>Note: The dataset given are protected with password, and the password is available in our published paper</p>
State of Practice and Challenges of Issue Management for Component-based and Service-Oriented Systems: An Empirical Study
<p>Modern software systems often consist of independently developed components, e.g., libraries or (micro-)services, that can be combined to form a larger architecture. Many of these components are developed externally, i.e., in other projects by other teams, which is particularly the case in service-oriented architectures. Even though the components are independently developed, bugs may arise that propagate along the call chains, which sometimes negatively affect the calling components. However, as such components are typically managed in separate issue management systems (IMS), identifying that a downstream component is affected by an upstream component's issues is complex and time-consuming. Even though some scientific works and IMS forums discuss how to manage such cross-component issues, there is no systematic study on the state of practice and the challenges of issue management when different components are affected. Therefore, this study aims to determine which types of components are usually included in modern software projects, how developers identify and manage issue propagation between components, and how often they face such propagations. To get these insights, we conducted an empirical study through a questionnaire with 64 industry experts and open-source contributors and interviewed seven of them. Our key takeaways help researchers and software engineers to understand possible impacts and to improve cross-component issue management. We identified, for example, that related issues currently are non-semantically linked via URLs in issue comments.</p>
Data from an empirical study on generative artificial intelligence painting
<p>This is an empirical study on generative artificial intelligence painting, including research methods, basic data of 17 subjects and their AI art painting, and quantitative data after their data analysis.</p>
Dataset of "What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation"
<p>Dataset of <em>What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation</em> (Martin Monperrus, Michael Eichberg, Elif Tekes, Mira Mezini), In Empirical Software Engineering, Springer, 2011.</p> <p><br> * dataset-src.tar.bz2 contains the source code of the Java libraries used as raw data.<br> * dataset.xml.bz2 contains the API documentation extracted from source code.<br> * directives.xml.bz2 contains the API directives found during the exploratory case study.<br> * directive-appendix.pdf is a human-readable PDF version of directives.xml.bz2.<br> <br> All datasets are published under the Creative Commons Attribution License: if you use them, please cite:<br> </p>
What does Google recommend when you want to compare insurance offerings? – A method and empirical study considering Google's top search results
<p>This dataset is part of a publication and shows Google's search results for German search queries on insurance comparison offerings.</p> <p>Relevant search queries were extracted from a commercial search engine log file consisting of more than 640,000 different search queries. From the log, we extracted a variety of query formulations for the same topic, i.e., queries containing the same word or phrase. The selection was based on pre-defined keywords in the context of insurance comparisons. The queries from the log file were automatically selected by combining the terms "*insurance*" and "*comparison*" (including left as well as right truncation). Examples of such inquiries are "car insurance comparison", "occupational disability insurance comparison", "liability insurance in comparison". This procedure identified a total of 121 different search queries. Scraping of the results took place between 08.05. - 09.05.2018.The adress data were extracted by using a text classification algorithm and a crawler to find the contact data on a website.</p> <p>It is a tab-separated file with the following attributes:</p> <p>ID: Unique row identifier</p> <p>ID Query: Unique search query identifier</p> <p>Query: German search query </p> <p>Position: Result position to the search query </p> <p>URL: URL of the search result </p> <p>Host: Host of the search result </p> <p>Company: Name of the company on the website</p> <p>Street: Street in the address on the website </p> <p>Zipcode: Street in the address on the website </p> <p>Location: Location in the address on the website </p> <p>District: District in the address on the website </p> <p>State: State in the address on the website </p> <p>Country: Country in the address on the website </p>
Exploring Internal Quality Metric Fluctuations in Revision Histories – An Empirical Study
<p>Datasets and analysis script for the article "Exploring Internal Quality Metric Fluctuations in Revision Histories – An Empirical Study" submitted to ESEM'19 under double-blind review and provided as-is.</p>
Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
<p>Biological processes exhibit complex temporal dependencies due to the sequential nature of allocation decisions in organisms' life-cycles, feedback loops, and two-way causality. Consequently, longitudinal data often contain cross-lags: the predictor variable depends on the response variable of the previous time-step. Although statisticians have warned that regression models that ignore such covariate endogeneity in time series are likely to be inappropriate, this has received relatively little attention in biology. Furthermore, the resulting degree of estimation bias remains largely unexplored.</p> <p>We use a graphical model and numerical simulations to understand why and how regression models that ignore cross-lags can be biased, and how this bias depends on the length and number of time series. Ecological and evolutionary examples are provided to illustrate that cross-lags may be more common than is typically appreciated and that they occur in functionally different ways.</p> <p>We show that routinely used regression models that ignore cross-lags are asymptotically unbiased. However, this offers little relief, as for most realistically feasible lengths of time series conventional methods are biased. Furthermore, collecting time series on multiple subjects–such as populations, groups or individuals—does not help to overcome this bias when the analysis focusses on within-subject patterns (often the pattern of interest). Simulations (R tutorial 1 & 2), a literature search and a real-world empirical example on fairy wrens (data archived here with analyses presented in R-tutorial 3) together suggest that approaches that ignore cross-lags are likely biased in the direction opposite to the sign of the cross-lag (e.g. towards detecting density-dependence of vital rates and against detecting life history trade-offs and benefits of group living). Next, we show that multivariate (e.g. structural equation) models can dynamically account for cross-lags, and simultaneously address additional bias induced by measurement error, but only if the analysis considers multiple time series.</p> <p>We provide guidance on how to identify a cross-lag and subsequently specify it in a multivariate model, which can be far from trivial. Our tutorials with data and R code of the worked examples provide step‐by‐step instructions on how to perform such analyses.</p> <p>Our study offers insights into situations in which cross-lags can bias analysis of ecological and evolutionary time series and suggests that adopting dynamical models can be important, as this directly affects our understanding of population regulation, the evolution of life histories and cooperation, and possibly many other topics. Determining how strong estimation bias due to ignoring covariate endogeneity has been in the ecological literature requires further study, also because it may interact with other sources of bias.</p>
How Do I Refactor This? An Empirical Study on Refactoring Trends and Topics in Stack Overflow
<p>This is the dataset that accompanies the study: "<strong>How Do I Refactor This? An Empirical Study on Refactoring Trends and Topics in Stack Overflow</strong>." This study has been accepted for publication in the software engineering journal Empirical Software Engineering.</p> <p> </p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>An essential part of software maintenance and evolution, refactoring is performed by developers, regardless of technology or domain, to improve the internal quality of the system, and reduce its technical debt. However, choosing the appropriate refactoring strategy is not always straightforward, resulting in developers seeking assistance. Although research in refactoring is well-established, with several studies altering between the detection of refactoring opportunities and the recommendation of appropriate code changes, little is known about their adoption in practice. Analyzing the perception of developers is critical to understand better what developers consider to be problematic in their code and how they handle it. Additionally, there is a need for bridging the gap between refactoring, as research, and its adoption in practice, by extracting common refactoring intents that are more suitable for what developers face in reality. In this study, we analyze refactoring discussions on Stack Overflow through a series of quantitative and qualitative experiments. Our results show that Stack Overflow is utilized by a diverse set of developers for refactoring assistance for a variety of technologies. Our observations show five areas that developers typically require help with refactoring-- Code Optimization, Tools and IDEs, Architecture and Design Patterns, Unit Testing, and Database. We envision our findings better bridge the support between traditional (or academic) aspects of refactoring and their real-world applicability, including better tool support.</p> <p><strong>Manuscript DOI:</strong> <a href="https://doi.org/10.1007/s10664-021-10045-x">https://doi.org/10.1007/s10664-021-10045-x</a> </p>
Empirical Research on Requirements Quality: A Systematic Mapping Study - Dataset
<p>We conducted a systematic mapping study on the empirical research into the quality of requirements titled "Empirical Research on Requirements Quality: A SystematicMapping Study" in the Requirements Engineering Journal. This is our replication package.</p> <p>Full Abstract:</p> <p>Research has repeatedly shown that high quality requirements are essential for the success of development projects. While the term “quality” is pervasive in the field of requirements engineering and while the body of research on requirements quality is large, there is no meta study of the field that overviews and compares the concrete quality attributes addressed by the community. To fill this knowledge gap, we conducted a systematic mapping study of the scientific literature. We retrieved 6,905 articles from six academic databases, which we filtered down to our 105 relevant primary studies: explicitly defining, improving, or evaluating quality attributes while including an empirical research component. We found that research on requirements quality focuses on improvement techniques, with very few primary studies addressing evidence-based definitions and evaluations of quality attributes. Among the 12 quality attributes identified, the most prominent in the field are ambiguity, completeness, consistency, and correctness. We identified 111 sub-types of quality attributes such as “template conformance” for consistency or “passive voice” for ambiguity. Ambiguity has the largest share of these sub-types. The artefacts being studied are mostly referred to in the broadest sense as “requirements,” while little research targets quality attributes in specific types of requirements such as use cases or user stories. We present and discuss our detailed analysis along the various quality attributes. Our findings highlightthe need to conduct more grounded research aimed at “definitions,” to use more diverse research methods, and to address a more diverse set of requirements types.</p>
Raw videos of the empirical observations performed studying large shoals of sulphur mollies (P. sulphuraria).
<p>Data acquired between April 6th and April 11th 2018 in Teapa, Tabasco, Mexico. These dataset was obtained for the study of the spontaneous wave activity observed in large shoals of sulphur mollies. Details on the analysis can be found in DOI: XX.XXXX/XXX (currently under review).</p> <p>The file called <a href="https://zenodo.org/api/files/3278318f-de77-4ee2-b1f2-0e7b6c6178aa/data_ALL_clusters.csv?versionId=e11e17f5-ac27-421c-bc83-09a2ea08dd1e">data_ALL_clusters.csv</a> contains four columns with the following information:</p> <ol> <li>cluster ID</li> <li>cluster area</li> <li>cluster volume</li> <li>cluster time duration</li> <li>cluster speed</li> </ol> <p>The h5 files contain the same information separated by acquisition day. In these h5 files, the areas, volumes and times are given in number of pixels and/or number of frames. Thus, they have to be re-scaled with the scaling factor dx=0.02 and dt=1/50.</p>
Empirical Study on Test Generation Using GitHub Copilot --- Replication Package
<p>This replication package contains the data and scripts used in the "Empirical Study on Test Generation Using GitHub Copilot" thesis. </p>
The Relationship Between Different Python Argument-Passing Mechanisms and Fixes: An Empirical Study
<p>Replication package for the paper: The Relationship Between Different Python Argument-Passing Mechanisms and Fixes -- An Empirical Study</p>
The Pollution from Obsolete Issue Report: An Empirical Study
<p>The Pollution from Obsolete Issue Report: An Empirical Study</p> <p>Project summary</p> <p>In software development, programmers use issue trackers to manage their maintenance issues and record valuable maintenance details in issue reports. Based on these issue reports, programmers have enhanced code comprehension and researchers have mined knowledge from issue reports to assist various programming tasks. Although issue reports are useful, some of them can be obsolete, in that their corresponding commits are overwritten or rolled back, with the evolution of software. The obsolete issue reports can invalidate their references and descriptions, and can have far-reaching impacts on the approaches built on them.</p> <p>To deepen the understanding of obsolete issue reports, we conducted the first empirical study to analyze obsolete issue reports. We consider that an issue report is obsolete if its revisions are partially or totally removed in later commits. To measure how an issue report becomes obsolete, we define an obsolete ratio of an issue report as its deleted lines over all its modified lines. In this paper, we build a tool, ICLINKER, to inspect the obsolete issue reports and calculate the obsolete ratios. With ICLINKER, we analyze 70,180 commits and 46,257 issue reports that are collected from five Apache projects. Taking them as our inputs, we explore four research questions, which concern the distributions, the references, and the explanations of obsolete issue reports. Our findings on these research questions enrich the knowledge of obsolete issue reports, and some are even counterintuitive. For example, we find that obsolete issue reports are mixed with other issue reports. As another example, we find that only a small portion of issue reports are mentioned in code comments, but about half of them are obsolete. Based on our results, we analyze some directions that are worthy of exploration.</p> <p>Our identified obsolete ratios</p> <p>We identified the obsolete ratios of the issue reports from five projects. Their obsolete ratios are as follows: calcite.txt, cassandra.txt, derby.txt, hbase.txt, and hive.txt.</p> <p>Our Dataset</p> <p>Our dataset is stored in the data folder.</p>
Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
Open the record for dataset details and reuse information.
Data for empirical example in: An effect size for comparing the strength of morphological integration across studies
Open the record for dataset details and reuse information.
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.