Research Article
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Year 2020, Volume: 41 Issue: 3, 720 - 724, 30.09.2020
https://doi.org/10.17776/csj.728932

Abstract

References

  • V. Cosentino, J. Luis, and J. Cabot. Findings from GitHub: methods, datasets and limitations. Proceedings of the 13th International Workshop on Mining Software Repositories, (2016), 137–141.
  • V. Cosentino, J. L. Canovas Izquierdo, and J. Cabot. A Systematic Mapping Study of Software Development With GitHub. IEEE Access, 5 (2017) 7173–7192.
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  • G. Gousios. The GHTorrent dataset and tool suite. Proceedings of the 10th Working Conference on Mining Software Repositories, (2013) 233–236.
  • Y. Zhang, G. Yin, Y. Yu, and H. Wang. Investigating social media in GitHub’s pull-requests: a case study on Ruby on Rails. Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies - CrowdSoft 2014 (2014) 37–41.
  • E. van der Veen, G. Gousios, and A. Zaidman. Automatically Prioritizing Pull Requests. 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories. (2015) 357–361.
  • Y. Yu, H. Wang, V. Filkov, P. Devanbu, and B. Vasilescu. Wait for It: Determinants of Pull Request Evaluation Latency on GitHub. 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, (2015) 367–371.
  • M. L. de L. Júnior, D. M. Soares, A. Plastino, and L. Murta. Automatic assignment of integrators to pull requests: The importance of selecting appropriate attributes. J. Syst. Softw., 144 (2018) 181–196.
  • G. Zhao, D. A. da Costa, and Y. Zou. Improving the Pull Requests Review Process Using Learning-to-rank Algorithms. Empir. Softw. Eng., (2019) 1–31.

Summarising big data: public GitHub dataset for software engineering challenges

Year 2020, Volume: 41 Issue: 3, 720 - 724, 30.09.2020
https://doi.org/10.17776/csj.728932

Abstract

In open-source software development environments; textual, numerical, and relationship-based data generated are of interest to researchers. Various data sets are available for this data, which is frequently used in areas such as software engineering and natural language processing. However, since these data sets contain all the data in the environment, the problem arises in the terabytes of data processing. For this reason, almost all of the studies using GitHub data use filtered data according to certain criteria. In this context, using a different data set in each study makes a comparison of the accuracy of the studies quite difficult. In order to solve this problem, a common dataset was created and shared with the researchers, which would allow to work on many software engineering problems.

References

  • V. Cosentino, J. Luis, and J. Cabot. Findings from GitHub: methods, datasets and limitations. Proceedings of the 13th International Workshop on Mining Software Repositories, (2016), 137–141.
  • V. Cosentino, J. L. Canovas Izquierdo, and J. Cabot. A Systematic Mapping Study of Software Development With GitHub. IEEE Access, 5 (2017) 7173–7192.
  • Z. Kotti and D. Spinellis. Standing on shoulders or feet?: the usage of the MSR data papers. Proceedings of the 16th International Conference on Mining Software Repositories, (2019) 565–576.
  • G. Gousios. The GHTorrent dataset and tool suite. Proceedings of the 10th Working Conference on Mining Software Repositories, (2013) 233–236.
  • Y. Zhang, G. Yin, Y. Yu, and H. Wang. Investigating social media in GitHub’s pull-requests: a case study on Ruby on Rails. Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies - CrowdSoft 2014 (2014) 37–41.
  • E. van der Veen, G. Gousios, and A. Zaidman. Automatically Prioritizing Pull Requests. 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories. (2015) 357–361.
  • Y. Yu, H. Wang, V. Filkov, P. Devanbu, and B. Vasilescu. Wait for It: Determinants of Pull Request Evaluation Latency on GitHub. 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, (2015) 367–371.
  • M. L. de L. Júnior, D. M. Soares, A. Plastino, and L. Murta. Automatic assignment of integrators to pull requests: The importance of selecting appropriate attributes. J. Syst. Softw., 144 (2018) 181–196.
  • G. Zhao, D. A. da Costa, and Y. Zou. Improving the Pull Requests Review Process Using Learning-to-rank Algorithms. Empir. Softw. Eng., (2019) 1–31.
There are 9 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Engineering Sciences
Authors

Abdulkadir Şeker 0000-0002-4552-2676

Banu Diri 0000-0002-4052-0049

Halil Arslan 0000-0003-3286-5159

Fatih Amasyalı 0000-0002-0404-5973

Publication Date September 30, 2020
Submission Date April 29, 2020
Acceptance Date June 12, 2020
Published in Issue Year 2020Volume: 41 Issue: 3

Cite

APA Şeker, A., Diri, B., Arslan, H., Amasyalı, F. (2020). Summarising big data: public GitHub dataset for software engineering challenges. Cumhuriyet Science Journal, 41(3), 720-724. https://doi.org/10.17776/csj.728932