Name: Long Lived Bug Prediction
Creator: Kaggle
License: https://creativecommons.org/licenses/by/4.0/

About this Dataset

Long Lived Bug Prediction

Context

Authors have created this dataset to support the research on the long-lived bugs. It stores a set of bug reports opened and closed during the last seven years, extracted from six popular Free/Libre Open Source Software (FLOSS) projects: Eclipse, Freedesktop, GCC, Gnome, Mozilla, and WinHQ. The researchers can explore bug data in this dataset to understand different questions on bugs' nature. For instance, they can use it to correlate the bug fixing time with bug characteristics. The attributes in the dataset are described in the attached README.md file.

Acknowledgements

Gomes, Luiz; Torres, Ricardo; Côrtes, Mario (2021), “A Dataset for Long-lived Bug Prediction in FLOSS ”, Mendeley Data, V2, doi: 10.17632/v446tfssgj.2

Tables

Eclipse Bug Report Data

@kaggle.saurabhshahane_long_lived_bug_prediction.eclipse_bug_report_data

4.62 MB
9,776 rows
19 columns

CREATE TABLE eclipse_bug_report_data (
  "bug_id" VARCHAR,
  "creation_date" TIMESTAMP,
  "component_name" VARCHAR,
  "product_name" VARCHAR,
  "short_description" VARCHAR,
  "long_description" VARCHAR,
  "assignee_name" VARCHAR,
  "reporter_name" VARCHAR,
  "resolution_category" VARCHAR,
  "resolution_code" BIGINT,
  "status_category" VARCHAR,
  "status_code" BIGINT,
  "update_date" TIMESTAMP,
  "quantity_of_votes" BIGINT,
  "quantity_of_comments" BIGINT,
  "resolution_date" TIMESTAMP,
  "bug_fix_time" BIGINT,
  "severity_category" VARCHAR,
  "severity_code" BIGINT
);

Freedesktop Bug Report Data

@kaggle.saurabhshahane_long_lived_bug_prediction.freedesktop_bug_report_data

5.26 MB
7,684 rows
19 columns

CREATE TABLE freedesktop_bug_report_data (
  "bug_id" VARCHAR,
  "creation_date" TIMESTAMP,
  "component_name" VARCHAR,
  "product_name" VARCHAR,
  "short_description" VARCHAR,
  "long_description" VARCHAR,
  "assignee_name" VARCHAR,
  "reporter_name" VARCHAR,
  "resolution_category" VARCHAR,
  "resolution_code" BIGINT,
  "status_category" VARCHAR,
  "status_code" BIGINT,
  "update_date" TIMESTAMP,
  "quantity_of_votes" BIGINT,
  "quantity_of_comments" BIGINT,
  "resolution_date" TIMESTAMP,
  "bug_fix_time" BIGINT,
  "severity_category" VARCHAR,
  "severity_code" BIGINT
);

Gcc Bug Report Data

@kaggle.saurabhshahane_long_lived_bug_prediction.gcc_bug_report_data

9.27 MB
10,000 rows
19 columns

CREATE TABLE gcc_bug_report_data (
  "bug_id" VARCHAR,
  "creation_date" TIMESTAMP,
  "component_name" VARCHAR,
  "product_name" VARCHAR,
  "short_description" VARCHAR,
  "long_description" VARCHAR,
  "assignee_name" VARCHAR,
  "reporter_name" VARCHAR,
  "resolution_category" VARCHAR,
  "resolution_code" BIGINT,
  "status_category" VARCHAR,
  "status_code" BIGINT,
  "update_date" TIMESTAMP,
  "quantity_of_votes" BIGINT,
  "quantity_of_comments" BIGINT,
  "resolution_date" TIMESTAMP,
  "bug_fix_time" BIGINT,
  "severity_category" VARCHAR,
  "severity_code" BIGINT
);

Gnome Bug Report Data

@kaggle.saurabhshahane_long_lived_bug_prediction.gnome_bug_report_data

6.34 MB
7,813 rows
19 columns

CREATE TABLE gnome_bug_report_data (
  "bug_id" VARCHAR,
  "creation_date" TIMESTAMP,
  "component_name" VARCHAR,
  "product_name" VARCHAR,
  "short_description" VARCHAR,
  "long_description" VARCHAR,
  "assignee_name" VARCHAR,
  "reporter_name" VARCHAR,
  "resolution_category" VARCHAR,
  "resolution_code" BIGINT,
  "status_category" VARCHAR,
  "status_code" BIGINT,
  "update_date" TIMESTAMP,
  "quantity_of_votes" BIGINT,
  "quantity_of_comments" BIGINT,
  "resolution_date" TIMESTAMP,
  "bug_fix_time" BIGINT,
  "severity_category" VARCHAR,
  "severity_code" BIGINT
);

Mozilla Bug Report Data

@kaggle.saurabhshahane_long_lived_bug_prediction.mozilla_bug_report_data

4.14 MB
9,999 rows
19 columns

CREATE TABLE mozilla_bug_report_data (
  "bug_id" VARCHAR,
  "creation_date" TIMESTAMP,
  "component_name" VARCHAR,
  "product_name" VARCHAR,
  "short_description" VARCHAR,
  "long_description" VARCHAR,
  "assignee_name" VARCHAR,
  "reporter_name" VARCHAR,
  "resolution_category" VARCHAR,
  "resolution_code" BIGINT,
  "status_category" VARCHAR,
  "status_code" BIGINT,
  "update_date" TIMESTAMP,
  "quantity_of_votes" BIGINT,
  "quantity_of_comments" BIGINT,
  "resolution_date" TIMESTAMP,
  "bug_fix_time" BIGINT,
  "severity_category" VARCHAR,
  "severity_code" BIGINT
);

Winehq Bug Report Data

@kaggle.saurabhshahane_long_lived_bug_prediction.winehq_bug_report_data

3.3 MB
6,074 rows
19 columns

CREATE TABLE winehq_bug_report_data (
  "bug_id" VARCHAR,
  "creation_date" TIMESTAMP,
  "component_name" VARCHAR,
  "product_name" VARCHAR,
  "short_description" VARCHAR,
  "long_description" VARCHAR,
  "assignee_name" VARCHAR,
  "reporter_name" VARCHAR,
  "resolution_category" VARCHAR,
  "resolution_code" BIGINT,
  "status_category" VARCHAR,
  "status_code" BIGINT,
  "update_date" TIMESTAMP,
  "quantity_of_votes" BIGINT,
  "quantity_of_comments" BIGINT,
  "resolution_date" TIMESTAMP,
  "bug_fix_time" BIGINT,
  "severity_category" VARCHAR,
  "severity_code" BIGINT
);