CAM: A Collection of Snapshots of GitHub Java Repositories Together with Metrics

Temel Kavramlar

CAM project provides a comprehensive dataset of Java repositories from GitHub along with essential metrics, aiding researchers in replicability and reducing data preprocessing efforts.

Özet

The CAM project addresses the need for stable datasets by cloning Java repositories from GitHub, filtering out unnecessary files, parsing Java classes, and computing various metrics. The dataset is generated annually and published on Amazon S3 for researchers' reference. The archive includes 532K Java classes with 48 metrics each. Research projects often face challenges in ensuring the replicability of results due to the volatile nature of source code. CAM aims to reduce duplication of work by providing a ready-to-use archive of downloaded, filtered, and measured source code files. Limitations include the inability to analyze all Java repositories on GitHub and modifications made to original metric algorithms due to modern Java features.

İstatistikler

The latest archive size is 2.2Gb.
It includes 532K Java classes with 48 metrics for each class.
It took 10 days on a server with eight vCPU and 32Gb of RAM to generate the data.

Alıntılar

"Having a ready-to-use archive of downloaded, filtered, and measured source code files would help many research projects reduce the amount of work required."
"We expect CAM archives to be used by research teams analyzing Java source code."

Önemli Bilgiler Şuradan Elde Edildi

CAM

by Yegor Bugaye... : arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08488.pdf

Daha Derin Sorular

How can researchers ensure the representativeness of their results when using a limited dataset like CAM?

Researchers can ensure the representativeness of their results when utilizing a limited dataset like CAM by clearly acknowledging the limitations of the dataset. Understanding that only a small fraction of Java repositories on GitHub are being analyzed through CAM, researchers should be cautious in generalizing findings to the entire Java domain. It is crucial for researchers to clearly state these limitations in their research papers and explain that while CAM provides valuable insights, it may not capture the full diversity and complexity of all Java code available. Researchers should also consider complementing CAM data with additional sources or conducting sensitivity analyses to assess how robust their conclusions are across different datasets.

What are the implications of modifying original metric algorithms in terms of result accuracy?

Modifying original metric algorithms can have significant implications on result accuracy. When altering metrics from their original definitions due to modern language features or other factors, researchers must be aware that the computed metrics may deviate from what was initially intended by the creators. This could lead to inaccuracies in comparisons with existing literature or previous studies that used standard metric calculations. Researchers using modified algorithms should clearly document these changes and provide justifications for why modifications were necessary. Additionally, validation studies comparing results obtained with modified metrics against those obtained with standard metrics could help assess any potential biases introduced by alterations.

How can open-source communities contribute effectively to enhancing CAM scripts?

Open-source communities can contribute effectively to enhancing CAM scripts by actively participating in script development, testing, and refinement processes. Community members can collaborate on improving filtering mechanisms within scripts to enhance data quality and relevance for research purposes. Additionally, they can work together to expand the range of code metrics collected by identifying new relevant metrics or refining existing ones based on community feedback and emerging trends in software engineering research.
Moreover, open-source contributors can assist in optimizing script performance and scalability so that larger datasets can be processed efficiently without compromising data integrity. By fostering a collaborative environment where developers share ideas, suggestions, and code contributions openly, open-source communities play a vital role in continuously improving CAM scripts' functionality and usability for diverse research projects within the software engineering domain.