File Validation And Training Statistics by Kaggle | Other

About this Dataset

File Validation And Training Statistics

File Validation and Training Statistics

Validation, Training, and Testing Statistics for tasksource/leandojo Files

By tasksource (From Huggingface) [source]

About this dataset

The tasksource/leandojo: File Validation, Training, and Testing Statistics dataset is a comprehensive collection of information regarding the validation, training, and testing processes of files in the tasksource/leandojo repository. This dataset is essential for gaining insights into the file management practices within this specific repository.

The dataset consists of three distinct files: validation.csv, train.csv, and test.csv. Each file serves a unique purpose in providing statistics and information about the different stages involved in managing files within the repository.

In validation.csv, you will find detailed information about the validation process undergone by each file. This includes data such as file paths within the repository (file_path), full names of each file (full_name), associated commit IDs (commit), traced tactics implemented (traced_tactics), URLs pointing to each file (url), and respective start and end dates for validation.

train.csv focuses on providing valuable statistics related to the training phase of files. Here, you can access data such as file paths within the repository (file_path), full names of individual files (full_name), associated commit IDs (commit), traced tactics utilized during training activities (traced_tactics), URLs linking to each specific file undergoing training procedures (url).

Lastly, test.csv encompasses pertinent statistics concerning testing activities performed on different files within the tasksource/leandojo repository. This data includes information such as file paths within the repo structure (file_path), full names assigned to each individual file tested (full_name) , associated commit IDs linked with these files' versions being tested(commit) , traced tactics incorporated during testing procedures regarded(traced_tactics) ,relevant URLs directing to specific tested files(url).

By exploring this comprehensive dataset consisting of three separate CSV files - validation.csv, train.csv, test.csv - researchers can gain crucial insights into how effective strategies pertaining to validating ,training or testing tasks have been implemented in order to maintain high-quality standards within the tasksource/leandojo repository

How to use the dataset

Familiarize Yourself with the Dataset Structure:

The dataset consists of three separate files: validation.csv, train.csv, and test.csv.

Each file contains multiple columns providing different information about file validation, training, and testing.

Explore the Columns:

'file_path': This column represents the path of the file within the repository.

'full_name': This column displays the full name of each file.

'commit': The commit ID associated with each file is provided in this column.

'traced_tactics': The tactics traced in each file are listed in this column.

'url': This column provides the URL of each file.

Understand Each File's Purpose:

Validation.csv

This file contains information related to the validation process of files in the tasksource/leandojo repository.

Train.csv

Utilize this file if you need statistics and information regarding the training phase of files in tasksource/leandojo repository.

Test.csv

For insights into statistics and information about testing individual files within tasksource/leandojo repository, refer to this file.

Generate Insights & Analyze Data:

Once you have a clear understanding of each column's purpose, you can start generating insights from your analysis using various statistical techniques or machine learning algorithms.

Explore patterns or trends by examining specific columns such as 'traced_tactics' or analyzing multiple columns together.

Combine Multiple Files (if necessary):

If required, you can merge/correlate data across different csv files based on common fields such as 'file_path', 'full_name', or 'commit'.

Visualize the Data (Optional):

To enhance your analysis, consider creating visualizations such as plots, charts, or graphs. Visualization can offer a clear representation of patterns or relationships within the dataset.

Obtain Further Information:

If you need additional details about any specific file, make use of the provided 'url' column to access further information.

Remember that this guide provides a general overview of how to utilize this dataset effectively. Feel free to explore different aspects and experiment with various approaches based on your specific objectives and requirements.

Happy analyzing!

Research Ideas

Analyzing the effectiveness of different tactics traced in the files: By using the dataset, researchers or developers can analyze the tactics traced in the files and evaluate their effectiveness in terms of file validation, training, or testing. This can help improve software development and testing processes.

Identifying patterns or trends in file validation, training, and testing: The dataset provides information about the start and end dates of file validation, training, or testing. By analyzing this data, one can identify any patterns or trends that may exist in terms of the duration of these processes for different files. This can provide insights into optimizing resource allocation and planning future software development projects.

Comparing statistics between different phases (validation vs training vs testing): The dataset contains separate files for validation, training, and testing statistics. By comparing these statistics, one can gain a better understanding of how each phase contributes to overall file quality and performance metrics. This information can be used to make informed decisions about allocating resources or improving specific phases based on their impact on final outcomes

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv

Column name	Description
file_path	The path within the repository where the file is located. (String)
full_name	The complete name of the file. (String)
commit	The unique identifier associated with a specific version or revision of a file. (String)
traced_tactics	The tactics identified or observed in each file during validation, training, or testing. (String)
url	The web address linking directly to the respective file. (String)
end	The date and time when the validation, training, or testing process ended. (Datetime)
start	The date and time when the validation, training, or testing process started. (Datetime)

File: train.csv

Column name	Description
file_path	The path within the repository where the file is located. (String)
full_name	The complete name of the file. (String)
commit	The unique identifier associated with a specific version or revision of a file. (String)
traced_tactics	The tactics identified or observed in each file during validation, training, or testing. (String)
url	The web address linking directly to the respective file. (String)
end	The date and time when the validation, training, or testing process ended. (Datetime)
start	The date and time when the validation, training, or testing process started. (Datetime)

File: test.csv

Column name	Description
file_path	The path within the repository where the file is located. (String)
full_name	The complete name of the file. (String)
commit	The unique identifier associated with a specific version or revision of a file. (String)
traced_tactics	The tactics identified or observed in each file during validation, training, or testing. (String)
url	The web address linking directly to the respective file. (String)
end	The date and time when the validation, training, or testing process ended. (Datetime)
start	The date and time when the validation, training, or testing process started. (Datetime)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit tasksource (From Huggingface).

Tables

Test

@kaggle.thedevastator_file_validation_and_training_statistics.test

576.68 KB
2000 rows
7 columns


CREATE TABLE test (
  "file_path" VARCHAR,
  "full_name" VARCHAR,
  "commit" VARCHAR,
  "traced_tactics" VARCHAR,
  "url" VARCHAR,
  "end" VARCHAR,
  "start" VARCHAR
);

Train

@kaggle.thedevastator_file_validation_and_training_statistics.train

25.37 MB
87766 rows
7 columns


CREATE TABLE train (
  "file_path" VARCHAR,
  "full_name" VARCHAR,
  "commit" VARCHAR,
  "traced_tactics" VARCHAR,
  "url" VARCHAR,
  "end" VARCHAR,
  "start" VARCHAR
);

Validation

@kaggle.thedevastator_file_validation_and_training_statistics.validation

634 KB
2000 rows
7 columns


CREATE TABLE validation (
  "file_path" VARCHAR,
  "full_name" VARCHAR,
  "commit" VARCHAR,
  "traced_tactics" VARCHAR,
  "url" VARCHAR,
  "end" VARCHAR,
  "start" VARCHAR
);