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1,034 results for “Empirical”
Cognition in Social Engineering Empirical Research: a Systematic Literature Review
<p># Description of contents</p> <p>This repository contains the codebook, dataset and analysis scripts in R used for the following publication: Pavlo Burda, Luca Allodi, Nicola Zannone. "Cognition in Social Engineering Empirical Research: a Systematic Literature Review". ACM Transactions on Computer-Human Interaction (TOCHI).<br> The repository consists of the following files:</p> <p>## dataset_and_codebook.xlsx contains the dataset, the codebook and a detailed description of contents.</p> <p>## scripts/ contains the R scripts used for the analysis</p> <p>## readme.txt contains this readme</p> <p><br> # Dataset and codebook</p> <p>The dataset_and_codebook.xlsx contains the following sheets:</p> <p>## Codebook<br> Contains a detailed description of the dataset (tables, columns, fields, etc.).<br> The codebook describes the concepts and variables that are present in the dataset. This includes explanations on meaning, numerical values, classification schemes and labels.</p> <p>## Hypotheses table <br> Contains the hypotheses for all analyzed papers and is used in the results of the paper. <br> Each row is a hypothesis of an included paper, cells contain one or more values (e.g., value1,value2,...) with or without sub values (e.g., value1,value2(sub-value1, ...)). Any content that is in square brackets [] is ignored in the analysis. Empty cells mean that there is no applicable value for that column.</p> <p>## Papers table<br> Contains the analyzed papers and is used in the results of the paper. It is also used in the overview table (Table 6) in Appendix C.<br> Each row is an included paper, cells contain up to two values (e.g., value1, value2) or the word 'multiple' in case of more than two values. Empty cells mean that there is no applicable value for that column.</p> <p>## Values table <br> Contains the description of cell values of 'Hypotheses' and 'Papers' and sub-values (specific variables in a study) that belong to a value. <br> It has a hierarchical structure from left to right where each field on the right column falls under the first non-empty field on the immediate left-top.</p> <p><br> # Reproducing results with scripts/ (generate figures)<br> To run the R scripts included in the 'scripts' directory it is sufficient to follow the instructions in and run the 'RUN_ALL_SCRIPTS.R' in 'scripts' directory.<br> The scripts use the TSV (Tab Separated Values) format of the dataset, which is the exact copy of the 'Papers' and 'Hypotheses' tables in the 'dataset_and_codebook.xlsx' file.<br> The resulting figures are stored in the 'scripts/results' directory.</p>
spanichella/RP_EMSE_MCR_2019 v.1.0.1 Second release of of the replication Package for the paper "An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code Review".
<p>Replication Package for the paper "An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code Review"</p> <p>Structure</p> <pre><code>project_raw_data/ gerrit_review_comments.csv gerrit_review_changes.csv survey_raw_data/ google_forms_survey.pdf google_forms_survey.csv RQ1_taxonomy_mcr/ RQ1_inception_phase/ initial_taxonomy.pdf intermediate_taxonomy.pdf RQ1_definition_phase/ Q1.2_evaluation_survey.csv cram_classified.csv cram.pdf RQ2_automation_needs/ Q2.1-Q2.5_evaluation_survey.xlsx Q2.6-Q2.7_evaluation_survey.xlsx Q2.1-Q2.7_question_index.csv Now "RQ3_automated_support/" contain the results concerning RQ2.1 in the paper. content explained in the README.md file located in "RQ3_automated_support/README.md" </code></pre> <p>Contents of the Replication Package</p> <p><strong>project-raw-data/</strong> contains the data used for the creation of our taxonomies, it includes information about the ten open-source projects.</p> <ul> <li><code>gerrit_review_comments.csv</code> - information about all in-line review comments used for this paper</li> <li><code>gerrit_review_changes.csv</code> - information about all patches analyzed that contain the in-line comments</li> </ul> <p><strong>survey_raw_data/</strong> contains information about the survey conducted for the paper.</p> <ul> <li><code>google_forms_survey.pdf</code> - the distributed <em>Google Forms</em> of our survey</li> <li><code>google_forms_survey.csv</code> - all survey answers obtained from 52 survey participants</li> </ul> <p><strong>RQ1_taxonomy_mcr/</strong> contains information and data about the elicited taxonomies in our paper (RQ1).</p> <ul> <li><strong>RQ1_inception_phase/</strong> <ul> <li><code>initial_taxonomy.pdf</code> - initial taxonomy obtained in the inception phase of our paper</li> <li><code>intermediate_taxonomy.pdf</code> - intermediate taxonomy after integrating and merging the initial taxonomy with the one by Beller <em>et al</em> [1]</li> </ul> </li> <li><strong>RQ1_definition_phase/</strong> <ul> <li><code>Q1.2_evaluation_survey.csv</code> - relevant survey feedback with additional taxonomy categories integrated into <em>CRAM</em></li> <li><code>cram_classified.csv</code> - classified review comments (<code>gerrit_review_comments.csv</code>) into <em>CRAM</em></li> <li><code>cram.pdf</code> - <em>CRAM</em> taxonomy</li> <li><code>cram_classified_with_frequency_information2020.xls</code> - it contain the information used to compute the frequency of CRAM changes, derived by the analysis of the 211 commits</li> </ul> </li> </ul> <p><strong>RQ2_automation_needs/</strong> contains the encoded evaluation of the survey question Q2.1-2.7 for RQ2</p> <ul> <li><code>Q2.1-Q2.5_evaluation_survey.xlsx</code> - the encoded evaluation of the survey questions Q2.1-Q2.5 (used for the <em>Automation Needs</em> Section in the paper) and contains the following sheets: <ul> <li><strong>all Findings</strong>: Very detailed findings matrix distilled from all answers concerning possible automated solutions or general possibilities to achieve automation in MCR. Every feedback was analyzed and decomposed into single findings. These findings are grouped, into categories of our Taxonomy of Code Changes in MCR (CRAM). Red represent in the feedback-text where the corresponding category was distilled from.</li> <li><strong>unique Findings</strong>: As one participant could mention the same categories/solutions in multiple feedbacks, the following matrix is cleaned of any duplication of participant answers. Multiple feedbacks containing the same information by one participant were removed, leaving only distinct occurances.</li> <li><strong>aggregated by Solution</strong>: Aggregated feeback clustered into abstracted solutions and the number of times participants mentioned the solution.</li> <li><strong>aggregated by Taxonomy</strong>: Aggregated feeback grouped by low-level categoried in CRAM.</li> </ul> </li> <li><code>Q2.6-Q2.7_evaluation_survey.xlsx</code> - the encoded evaluation of the survey questions Q2.6-Q2.7 (used for the <em>Automation Needs</em> Section in the paper) and contains the following sheets: <ul> <li><strong>all Findings</strong>: Very detailed findings matrix distilled from all answers concerning possible techniques, approaches and data to achieve automation in MCR. Every feedback was analyzed and decomposed into single findings. These findings are grouped, into categories of our Taxonomy of Code Changes in MCR (CRAM). Red represent in the feedback-text where the corresponding category was distilled from.</li> <li><strong>unique Findings</strong>: As one participant could mention the same categories/solutions in multiple feedbacks, the following matrix is cleaned of any duplication of participant answers. Multiple feedbacks containing the same information by one participant were removed, leaving only distinct occurances.</li> <li><strong>aggregated by low-level taxonomy</strong>: Aggregated mentionings of approaches/data by developers in the survey grouped by low-level taxonomy category.</li> <li><strong>aggregated by high-level taxonomy</strong>: Aggregated mentionings of approaches/data by developers in the survey grouped by high-level taxonomy category.</li> </ul> </li> <li><code>Q2.1-Q2.7_question_index.csv</code> - table of IDs given to each participant-question pair for Q2.1-Q2.7 in order to trace back the feeback.</li> <li><code>cram_survey-with_criticality_and_feasibility2020.xls</code> and <code>cram_survey-with_relevance_and_completeness_information2020.xls</code>: they contain we results of the survey, involving 14 additional participants (12 developers and 2 researchers), not involved in the aforementioned survey, and performed to qualitatively assess the relevance and completeness of the identified MCR change types as well as assess how critical and feasible to implement are some of the identified techniques to support MCR activities.</li> </ul> <p><strong>RQ2_1_automated_support/</strong> (or <strong>RQ3_automated_support/</strong> )- content explained in the README.md file located in "RP_EMSE_MCR_2019/tree/master/EMSE_MCR_2019/RQ3_automated_support/README.md"</p> <p>References</p> <p>[1] Moritz Beller, Alberto Bacchelli, Andy Zaidman, and Elmar Juergens. 2014. Modern code reviews in open-source projects: which problems do they fix?. In Proceedings of the 11th Working Conference on Mining Software Repositories (MSR 2014). ACM, New York, NY, USA, 202-211. DOI: <a href="http://dx.doi.org/10.1145/2597073.2597082">http://dx.doi.org/10.1145/2597073.2597082</a></p>
Dataset for "Architectural Security Weaknesses in Industrial Control Systems: An Empirical Study Based on Security Advisories' Vulnerability Reports"
<p>Supplementary artifacts to "<em>Architectural Security Weaknesses in Industrial Control Systems (ICS): An Empirical Study based on Disclosed Software Vulnerabilities</em>"</p> <p>Published in the Proceedings from the 2019 IEEE International Conference on Software Architecture (ICSA)</p> <p>Package Contains:</p> <p>- Raw output showing Components, CAWEs, and CVEs per report</p> <p>- Frequency Data (# of reports) for those concerns</p> <p>- ICS Component - Term Dictionary </p> <p>- HTML versions of reports studied in paper</p>
Original dataset for "A validation of co-authorship credit models with empirical data from the contributions of PhD candidates"
<p><strong>Publication reference:</strong><br> Donner, P. (2020). A validation of co-authorship credit models with empirical data from the contributions of PhD candidates. Quantitative Science Studies, v. 1, i. 2, p. 551-564. <a href="https://doi.org/10.1162/qss_a_00048">https://doi.org/10.1162/qss_a_00048</a>.</p> <p> </p> <p>The file contains one row per authorship contribution statement. Rows of publications and theses are grouped.</p> <p><strong>Description of columns:</strong></p> <p>dissertation_id - an integer identifying each dissertation thesis</p> <p>university - university at which the dissertation thesis was written and PhD degree conferred</p> <p>year - publication year of the dissertation thesis</p> <p>author - dissertation thesis author name</p> <p>title - dissertation thesis title</p> <p>subject - the field of research</p> <p>publication_id - an integer identifying each publication; publication associated with more than one thesis have the same id across theses</p> <p>reference - bibliographic reference for the publication associated with the thesis</p> <p>author_count - number of authors of the publication</p> <p>author_position - position in the author byline of the credited author</p> <p>credit - claimed credit of the author in percent</p> <p>corresponding_author - flag for whether the publication author of this row is a orresponding author</p>
Dataset: Breaking Type-Safety in Go: An Empirical Study on the Usage of the unsafe Package
<p>This dataset contains all script used in the study, as well as the raw data extracted from the repositories and the processed data used to analyze our RQs in the manuscript "Breaking Type-Safety in Go: An Empirical Study on the Usage of the unsafe Package".</p> <p>For more information on how to understand the folder structure, scripts, and dataset, please read the README.md. </p> <p> </p> <p> </p>
Supplementary Online Material to the paper: Modelling and empirical validation of carbon stock accumulation during the forest transition in France 1850-2015
<p><strong>Supplementary Online Material to the paper:</strong></p> <p><strong>Modelling and empirical validation of carbon stock accumulation during the forest transition in France 1850-2015</strong></p>
Artifact for the ESEC/FSE 2020 Paper: An Empirical Analysis of the Costs of Clone- and Platform-Oriented Software Reuse
<p>This dataset comprises the supplementary material for the paper "An Empirical Analysis of the Costs of Clone- and Platform-Oriented Software Reuse" by Jacob Krüger and Thorsten Berger, accepted at ESEC/FSE 2020.</p> <p>The dataset comprises:</p> <ul> <li>bibFilesManualSearch: The bib files for all venues analyzed, as provided by DBLP (cf. Section 2.4)</li> <li>dataFromPapers: The pdf file documents all included studies and the data extracted from these (cf. Section 2.4, 3.2, and 3.3)</li> <li>interviewGuide: The guide/questions for our semi-structured intreviews in the cost assessment phase (cf. Section 2.3)</li> <li>anonymizedInterviewSummary: The anonymized and summarized data from the cost-assessment interviews (cf. Section 3.2 and 3.3)</li> <li>R: Our R script for creating our figures and the corresponding csv files</li> </ul> <p> </p>
Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments -- part I
<p>Nearly all current Bayesian phylogenetic applications rely on Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution for trees and other parameters of the model. These approximations are only reliable if Markov chains adequately converge and sample from the joint posterior distribution. While several studies of phylogenetic MCMC convergence exist, these have focused on simulated datasets or select empirical examples. Therefore, much that is considered common knowledge about MCMC in empirical systems derives from a relatively small family of analyses under ideal conditions. To address this, we present an overview of commonly applied phylogenetic MCMC diagnostics and an assessment of patterns of these diagnostics across more than 18,000 empirical analyses. Many analyses appeared to perform well and failures in convergence were most likely to be detected using the average standard deviation of split frequencies, a diagnostic that compares topologies among independent chains. Different diagnostics yielded different information about failed convergence, demonstrating that multiple diagnostics must be employed to reliably detect problems. The number of taxa and average branch lengths in analyses have clear impacts on MCMC performance, with more taxa and shorter branches leading to more difficult convergence. We show that the usage of models that include both Γ-distributed among-site rate variation and a proportion of invariable sites are not broadly problematic for MCMC convergence but are also unnecessary. Changes to heating and the usage of model-averaged substitution models can both offer improved convergence in some cases, but neither are a panacea.</p>
Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments --part II
<p>Nearly all current Bayesian phylogenetic applications rely on Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution for trees and other parameters of the model. These approximations are only reliable if Markov chains adequately converge and sample from the joint posterior distribution. While several studies of phylogenetic MCMC convergence exist, these have focused on simulated datasets or select empirical examples. Therefore, much that is considered common knowledge about MCMC in empirical systems derives from a relatively small family of analyses under ideal conditions. To address this, we present an overview of commonly applied phylogenetic MCMC diagnostics and an assessment of patterns of these diagnostics across more than 18,000 empirical analyses. Many analyses appeared to perform well and failures in convergence were most likely to be detected using the average standard deviation of split frequencies, a diagnostic that compares topologies among independent chains. Different diagnostics yielded different information about failed convergence, demonstrating that multiple diagnostics must be employed to reliably detect problems. The number of taxa and average branch lengths in analyses have clear impacts on MCMC performance, with more taxa and shorter branches leading to more difficult convergence. We show that the usage of models that include both Γ-distributed among-site rate variation and a proportion of invariable sites are not broadly problematic for MCMC convergence but are also unnecessary. Changes to heating and the usage of model-averaged substitution models can both offer improved convergence in some cases, but neither are a panacea.</p>
Software Product Line Traceability and Product Configuration in Class and Sequence Diagrams: an Empirical Study
<p>Software Product Line Traceability and Product Configuration in Class and Sequence Diagrams: an Empirical Study</p>
Software Product Line Configuration and Traceability: an Empirical Study on SMarty Class and Component Diagrams
<p>Software Product Line Configuration and Traceability: an Empirical Study on SMarty Class and Component Diagrams</p>
FIG. 3 in Exploitation animale à l'Ancien Empire en Égypte: les apports d'Ayn Asil (oasis de Dakhla)
FIG. 3. — Proportions des principales catégories animales selon les différentes phases d'occupation du site (en pourcentage du nombre de restes déterminés). Abréviation: N, nombre de restes.
FIG. 4 in Exploitation animale à l'Ancien Empire en Égypte: les apports d'Ayn Asil (oasis de Dakhla)
FIG. 4. — Proportion relative des bovins selon les différentes phases en pour- centage du nombre de restes. Abréviation: N, Nombre de restes.
FIG. 5 in Exploitation animale à l'Ancien Empire en Égypte: les apports d'Ayn Asil (oasis de Dakhla)
FIG. 5. — Distribution des principales catégories animales selon les différents secteurs du site en nombre de restes: A, Phase I; B, Phase II. Abréviation: N, nombre de restes.
The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study
<p>Online appendix of the paper entitled: "The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study". It contains all scripts and data required to replicate the four research questions of the study.</p> <p>Abstract: Software vulnerabilities are weaknesses in source code that can be potentially exploited to cause loss or harm. While researchers have been devising a number of methods to deal with vulnerabilities, there is still a noticeable lack of knowledge on their software engineering life cycle, for example how vulnerabilities are introduced and removed by developers. This information can be exploited to design more effective methods for vulnerability prevention and detection, as well as to understand the granularity that these methods should aim at. To investigate the life cycle of software vulnerabilities, we focus on how, when, and under which circumstances vulnerabilities are introduced in software projects, as well as whether, after how long, and how they are removed. We consider 4,097 vulnerabilities with public patches from the National Vulnerability Database—pertaining to 1,163 open-source software projects on GITHUB—and define a six-step process that involves both automated parts (e.g., using the SZZ algorithm to find the vulnerability-inducing commits) and manual analyses (e.g., how vulnerabilities were fixed). The investigated vulnerabilities can be classified in 148 categories, take on average 4.19 commits before being introduced, and remain unfixed for a median of 1,506.50 commits and 691.50 days. Most of them are introduced by developers with high workload, often when doing maintenance activities, and removed with mostly with the addition of new source code aiming at implementing further checks on inputs. We conclude by distilling practical implications on when and how vulnerability detectors should work to better assist developers in early detecting these issues.</p>
On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects
<p>This is a replication package of our paper submission to the Saner 2021 entitled:</p> <p>On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects</p>
An Empirical Study of Flaky Tests in Python
<p><a href="https://arxiv.org/pdf/2101.09077.pdf">Paper</a></p> <p><a href="https://github.com/se2p/FlaPy">Flapy (our test execution tool)</a></p> <p>TestsOverview.csv<br> - Project Columns: Project_Name, Project_URL, Project_Hash<br> - Test Columns: Test_filename, Test_classname, Test_funcname, Test_parametrization<br> - Flaky_sameOrder_withinIteration: the test is non-order-dependent flaky<br> - Order-dependent: the test is order-dependent flaky<br> - Flaky_Infrastructure: the test shows infrastructure flakiness</p>
Data from: Temperature drives Zika virus transmission: evidence from empirical and mathematical models
<p>Temperature is a strong driver of vector-borne disease transmission. Yet, for emerging arboviruses we lack fundamental knowledge on the relationship between transmission and temperature. Current models rely on the untested assumption that Zika virus responds similarly to dengue virus, potentially limiting our ability to accurately predict the spread of Zika. We conducted experiments to estimate the thermal performance of Zika virus (ZIKV) in field-derived Aedes aegypti across eight constant temperatures. We observed strong, unimodal effects of temperature on vector competence, extrinsic incubation period, and mosquito survival. We used thermal responses of these traits to update an existing temperature-dependent model to infer temperature effects on ZIKV transmission. ZIKV transmission was optimized at 29oC, and had a thermal range of 22.7oC - 34.7oC. Thus, as temperatures move toward the predicted thermal optimum (29oC) due to climate change, urbanization, or seasonally, Zika could expand north and into longer seasons. In contrast, areas that are near the thermal optimum were predicted to experience a decrease in overall environmental suitability. We also demonstrate that the predicted thermal minimum for Zika transmission is 5oC warmer than that of dengue, and current global estimates on the environmental suitability for Zika are greatly over-predicting its possible range.</p>
USE-ME Empirical evaluation pilot study
<p>We report on the pilot assessment of the feasibility of the <strong>USE-ME tool </strong>with master students in computer science which were involved into a DSL course. In total there were four groups consisted of two or three participants which were developing the following DSLs:</p> <ul> <li><strong>DSL Spreadsheets </strong>- a DSL transforms an activity graph into a Gantt chart. The target users of this DSL are to be project managers.</li> <li><strong>Gestures Kinect</strong> - a DSL which supports specification of communication between navy users using a Kinect device. The target users of this DSL are to be a navy operators.</li> <li><strong>Peddy Paper</strong> - a DSL which creates several instances of personalised paddy papers. The target users are paddy paper builders.</li> <li><strong>Smart House </strong>- a DSL which supports a design of the elements and operations that the house can have. It is meant to be used by house owners. </li> </ul> <p>The evaluation consisted of three learning session. The first one took place after four weeks of the DSL development. Students were introduced to the usability evaluation during a 2h theoretical lecture. For next 2h were introduced with the USE-ME tool and were guided to perform installation and set up working environment. Also, the students were given a participation questionnaire to fill in and describe a purpose of their DSL. In the end of a session, they were given the background questionnaire to fill till the following session which was happening 1 week after. Following two weekly 4h sessions were consisting of USE-ME hands on. The students were introduced to the modelling activities followed by Visualino example. For each activity, the same student from the group was using the tool to create USE-ME models for their DSL, while other students from the group were helping in deciding what would be a right specification. Finally, students were asked to try to finish the learned models and deliver them as a part of DSL course. After delivery, the students which were using the tool to model were asked to fill in a feedback questionnaire. </p> <p>The evaluation deliveries related to each DSL (project reports, USE-ME models), and ones related to participants (background and feedback questionnaire), as well as evaluation results, are attached in this data set. </p>
An Empirical Study of Activity, Popularity, Size, Testing, and Stability in Continuous Integration
<p>A good understanding of the practices followed by software development projects can positively impact their success --- particularly for attracting talent and on-boarding new members. In this paper, we perform a cluster analysis to classify software projects that follow continuous integration in terms of their activity, popularity, size, testing, and stability. Based on this analysis, we identify and discuss four different groups of repositories that have distinct characteristics that separates them from the other groups. With this new understanding, we encourage open source projects to acknowledge and advertise their preferences according to these defining characteristics, so that they can recruit developers who share similar values.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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