1 dataset found
  1. Replication Kit: "Are Unit and Integration Test Definitions Still Valid for...

    • zenodo.org
    • explore.openaire.eu
    application/gzip, bin
    Updated Jan 24, 2020
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    Fabian Trautsch; Fabian Trautsch; Steffen Herbold; Jens Grabowski; Steffen Herbold; Jens Grabowski (2020). Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects" [Dataset]. http://doi.org/10.5281/zenodo.1415334
    Explore at:
    application/gzip, binAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Fabian Trautsch; Fabian Trautsch; Steffen Herbold; Jens Grabowski; Steffen Herbold; Jens Grabowski
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"
    This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets.

    Structure
    The structure of the replication kit is as follows:

    • additional_visualizations: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used
    • data_analysis: contains two python scripts that we used to analyze our raw data (one for each research question)
    • data_collection_tools: contains all source code used for the data collection, including the used versions of the COMFORT framework, the BugFixClassifier, and the used tools of the SmartSHARK environment;
    • mongodb_no_authors: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.


    Additional Visualizations
    We provide two additional visualizations for each project:
    1)

    For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both).

    Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type.


    Analysis scripts
    Requirements:
    - python3.5
    - tabulate
    - scipy
    - seaborn
    - mongoengine
    - pycoshark
    - pandas
    - matplotlib

    Both python files contain all code for the statistical analysis we performed.

    Data Collection Tools
    We provide all data collection tools that we have implemented and used throughout our paper. Overall it contains six different projects and one python script:

    • BugFixClassifier: Used to classify our defects.
    • comfort-core: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for these tests.
    • comfort-jacoco-listner: Used to intercept the coverage collection process as we were executing the tests of our case study projects.
    • issueSHARK: Used to collect data from the ITSs of the projects.
    • pycoSHARK: Library that contains models for the used ORM mapper that is used insight the SmartSHARK environment.
    • vcsSHARK: Used to collect data from the VCSs of the projects.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Fabian Trautsch; Fabian Trautsch; Steffen Herbold; Jens Grabowski; Steffen Herbold; Jens Grabowski (2020). Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects" [Dataset]. http://doi.org/10.5281/zenodo.1415334
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Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"

Explore at:
application/gzip, binAvailable download formats
Dataset updated
Jan 24, 2020
Dataset provided by
Zenodohttp://zenodo.org/
Authors
Fabian Trautsch; Fabian Trautsch; Steffen Herbold; Jens Grabowski; Steffen Herbold; Jens Grabowski
License

Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically

Description

Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"
This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets.

Structure
The structure of the replication kit is as follows:

  • additional_visualizations: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used
  • data_analysis: contains two python scripts that we used to analyze our raw data (one for each research question)
  • data_collection_tools: contains all source code used for the data collection, including the used versions of the COMFORT framework, the BugFixClassifier, and the used tools of the SmartSHARK environment;
  • mongodb_no_authors: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.


Additional Visualizations
We provide two additional visualizations for each project:
1)

For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both).

Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type.


Analysis scripts
Requirements:
- python3.5
- tabulate
- scipy
- seaborn
- mongoengine
- pycoshark
- pandas
- matplotlib

Both python files contain all code for the statistical analysis we performed.

Data Collection Tools
We provide all data collection tools that we have implemented and used throughout our paper. Overall it contains six different projects and one python script:

  • BugFixClassifier: Used to classify our defects.
  • comfort-core: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for these tests.
  • comfort-jacoco-listner: Used to intercept the coverage collection process as we were executing the tests of our case study projects.
  • issueSHARK: Used to collect data from the ITSs of the projects.
  • pycoSHARK: Library that contains models for the used ORM mapper that is used insight the SmartSHARK environment.
  • vcsSHARK: Used to collect data from the VCSs of the projects.

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