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26 results for “Packaging design”
Data Package Design for Special Cases
In this document we consider special cases for archiving research data based on their data type, format, or acquisition method, and recommend practices that ensure optimal re-usability of the data. Most recommendations here are aimed at improving documentation of data acquisition and processing to avoid misinterpretation. This includes the recommendation to publish raw data and/or processing code along with the data products. Others are aimed at usability in terms of data size/volume or connecting related data. Some recommendations involve including a metadata document formatted according to a new and emerging standard (e.g., codeMeta) or a data inventory table. Data inventory tables can cross the line between metadata and data and are intended to improve discoverability and navigation of archived data.
Replication Package for the paper: "How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study"
<p>This is the replication package for the paper: "How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study", published at the 36th International Conference on Software Maintenance and Evolution (ICSME' 20).</p> <p> </p> <p>It contains all the preliminary and final results of our empirical methodology. We highlight the manual classification of design-related and design-unrelated reviews, according to the developers’ intent of improving the structural design of the system. This might be used for further studies on the impact of design discussions on the structural quality of design.</p> <p> </p> <p>Feel free to use any part of this replication package in your study, please cite as:</p> <p>Anderson Uchôa, Caio Barbosa, Willian Oizumi, Publio Blenílio, Rafael Lima, Alessandro Garcia, and Carla Bezerra. How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study. Proceedings of the 36th International Conference on Software Maintenance and Evolution (ICSME), Adelaide, Australia, September 2020.</p>
Replication package with data used in the study: "The effect of code smells on the relationship between design patterns and defects. An empirical study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells on the relationship between design patterns and defects. An empirical study"</p> <p>This dataset contains the following folders:</p> <ul> <li> <p>"Analyzed systems" folder:</p> <ul> <li> <p>For each subject system (Ant-1.7, JEdit-4.2, Lucene-2.4, Camel-1.6, Log4j-1.2, Xalan-2.7, Poi-3.0, Ivy-2.0, Xerces-2.0, Velocity-1.6), we identify the following datasets: SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</p> </li> <li> <p>Those csv files include raw data about every class in every release. Each file includes columns which represent:</p> <ul> <li> <p>System: The analyzed system</p> </li> <li> <p>className: A fully qualified class name</p> </li> <li> <p>Pattern: if the class is part of any pattern the cell contains the name of the pattern, and “null” otherwise</p> </li> <li> <p>Smell: if the class is part of affected by any smell the cell contains the name of the smell, and “null” otherwise</p> </li> <li> <p>Bugs: Number of defects reported inside the class (extracted from the PROMISE dataset)</p> </li> </ul> </li> </ul> </li> <li> <p>A "detailed analysis" folder:</p> <ul> <li> <p>For each pattern, we report all the classes which participate in it in all the analyzed systems. The csv files inside this folder follow the same structure as the other csv files reported above</p> </li> </ul> </li> </ul> <p><br> </p>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
Design Factors for Evaluating Child Resistant Packaging
ClinicalTrials.gov study NCT03925623. IPD Sharing: YES. Countries: 1. Publications: 0.
ScienceDex guides
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