About this dataset

Context

The dataset tabulates the Java population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Java across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Java was 120, a 0.00% decrease year-by-year from 2021. Previously, in 2021, Java population was 120, a decline of 0.83% compared to a population of 121 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Java decreased by 75. In this period, the peak population was 195 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Java is shown in this column.
• Year on Year Change: This column displays the change in Java population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Java Population by Year. You can refer the same here

This dataset is based on the TravisTorrent dataset released 2017-01-11 (https://travistorrent.testroots.org), the Google BigQuery GHTorrent dataset accessed 2017-07-03, and the Git log history of all projects in the dataset, retrieved 2017-07-16 and 2017-07-17.

We selected projects hosted on GitHub that employ the Continuous Integration (CI) system Travis CI. We identified the projects using the TravisTorrent data set and considered projects that:

used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent),

were active for at least one year (365 days) before the first build with Travis CI (before_ci),

used Travis CI at least for one year (during_ci),

had commit or merge activity on the default branch in both of these phases, and

used the default branch to trigger builds.

To derive the time frames, we employed the GHTorrent Big Query data set. The resulting sample contains 113 projects. Of these projects, 89 are Ruby projects and 24 are Java projects. For our analysis, we only consider the activity one year before and after the first build.

We cloned the selected project repositories and extracted the version history for all branches (see https://github.com/sbaltes/git-log-parser). For each repo and branch, we created one log file with all regular commits and one log file with all merges. We only considered commits changing non-binary files and applied a file extension filter to only consider changes to Java or Ruby source code files. From the log files, we then extracted metadata about the commits and stored this data in CSV files (see https://github.com/sbaltes/git-log-parser).

We also retrieved a random sample of GitHub project to validate the effects we observed in the CI project sample. We only considered projects that:

have Java or Ruby as their project language

used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent)

have commit activity for at least two years (730 days)

are engineered software projects (at least 10 watchers)

were not in the TravisTorrent dataset

In total, 8,046 projects satisfied those constraints. We drew a random sample of 800 projects from this sampling frame and retrieved the commit and merge data in the same way as for the CI sample. We then split the development activity at the median development date, removed projects without commits or merges in either of the two resulting time spans, and then manually checked the remaining projects to remove the ones with CI configuration files. The final comparision sample contained 60 non-CI projects.

This dataset contains the following files:

tr_projects_sample_filtered_2.csv A CSV file with information about the 113 selected projects.

tr_sample_commits_default_branch_before_ci.csv tr_sample_commits_default_branch_during_ci.csv One CSV file with information about all commits to the default branch before and after the first CI build. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:

project: GitHub project name ("/" replaced by "_"). branch: The branch to which the commit was made. hash_value: The SHA1 hash value of the commit. author_name: The author name. author_email: The author email address. author_date: The authoring timestamp. commit_name: The committer name. commit_email: The committer email address. commit_date: The commit timestamp. log_message_length: The length of the git commit messages (in characters). file_count: Files changed with this commit. lines_added: Lines added to all files changed with this commit. lines_deleted: Lines deleted in all files changed with this commit. file_extensions: Distinct file extensions of files changed with this commit.

tr_sample_merges_default_branch_before_ci.csv tr_sample_merges_default_branch_during_ci.csv One CSV file with information about all merges into the default branch before and after the first CI build. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:

project: GitHub project name ("/" replaced by "_"). branch: The destination branch of the merge. hash_value: The SHA1 hash value of the merge commit. merged_commits: Unique hash value prefixes of the commits merged with this commit. author_name: The author name. author_email: The author email address. author_date: The authoring timestamp. commit_name: The committer name. commit_email: The committer email address. commit_date: The commit timestamp. log_message_length: The length of the git commit messages (in characters). file_count: Files changed with this commit. lines_added: Lines added to all files changed with this commit. lines_deleted: Lines deleted in all files changed with this commit. file_extensions: Distinct file extensions of files changed with this commit. pull_request_id: ID of the GitHub pull request that has been merged with this commit (extracted from log message). source_user: GitHub login name of the user who initiated the pull request (extracted from log message). source_branch : Source branch of the pull request (extracted from log message).

comparison_project_sample_800.csv A CSV file with information about the 800 projects in the comparison sample.

commits_default_branch_before_mid.csv commits_default_branch_after_mid.csv One CSV file with information about all commits to the default branch before and after the medium date of the commit history. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the commits tables described above.

merges_default_branch_before_mid.csv merges_default_branch_after_mid.csv One CSV file with information about all merges into the default branch before and after the medium date of the commit history. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the merge tables described above.

About this dataset

Context

The dataset tabulates the Java population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Java across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Java was 121, a 0% decrease year-by-year from 2022. Previously, in 2022, Java population was 121, an increase of 0.83% compared to a population of 120 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Java decreased by 74. In this period, the peak population was 195 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Java is shown in this column.
• Year on Year Change: This column displays the change in Java population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Java Population by Year. You can refer the same here