[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

What makes a popular academic AI repository?

  • Published:
Empirical Software Engineering Aims and scope Submit manuscript

Abstract

Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20% repositories that have the most number of stars as popular, and we label the bottom 70% repositories as unpopular. The remaining 10% repositories are set as a gap between popular and unpopular academic AI repositories. We propose 21 features to characterize the software engineering practices of academic AI repositories. Our experimental results show that popular and unpopular academic AI repositories are statistically significantly different in 11 of the studied features—indicating that the two groups of repositories have significantly different software engineering practices. Furthermore, we find that the number of links to other GitHub repositories in the README file, the number of images in the README file and the inclusion of a license are the most important features for differentiating the two groups of academic AI repositories. Our dataset and code are made publicly available to share with the community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://github.com/YuanruiZJU/academic-ai-repos

  2. https://github.com/jwyang/graph-rcnn.pytorch

  3. https://github.com/zziz/pwc

  4. https://github.com/foss2serve/github-workflow-activity

  5. https://github.com/svenpeter42/LightGBM-CEGB

  6. https://github.com/microsoft/LightGBM

  7. https://developer.github.com/v3/

  8. https://mistune.readthedocs.io/en/latest/

  9. https://pmd.github.io/latest/pmd_userdocs_cpd.HTML

  10. https://github.com/NVIDIA/vid2vid

  11. https://github.com/yunjey/stargan

  12. https://github.com/Artifineuro/zole

  13. https://github.com/fmcp/EndToEndIncrementalLearning

  14. https://github.com/yunjey/stargan

  15. https://github.com/NVIDIA/vid2vid

  16. https://cran.r-project.org/web/packages/vcd/index.html

  17. https://cran.r-project.org/web/packages/rms/index.html

  18. https://cran.r-project.org/web/packages/randomForest/index.html

  19. https://developer.github.com/v3/

  20. https://radimrehurek.com/gensim/

  21. https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/

  22. https://github.com/NVIDIA/vid2vid

  23. https://github.com/hujie-frank/SENet

  24. http://www.core.edu.au/conference-portal

References

  • Aggarwal K, Hindle A, Stroulia E (2014) Co-evolution of project documentation and popularity within github. In: Proceedings of the 11th working conference on mining software repositories. ACM, pp 360–363

  • Alves TL, Ypma C, Visser J (2010) Deriving metric thresholds from benchmark data. In: IEEE international conference on software maintenance. IEEE, pp 1–10

  • Balcan MF, Dick T, Sandholm T, Vitercik E (2018) Learning to branch. In: International conference on machine learning, pp 344–353

  • Bissyandé TF, Thung F, Lo D, Jiang L, Réveillere L (2013) Popularity, interoperability, and impact of programming languages in 100,000 open source projects. In: 2013 IEEE 37th annual computer software and applications conference. IEEE, pp 303–312

  • Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  • Boettiger C (2015) An introduction to docker for reproducible research. ACM SIGOPS Oper Syst Rev 49(1):71–79

    Article  Google Scholar 

  • Borges H, Hora A, Valente MT (2016a) Predicting the popularity of github repositories. In: Proceedings of the the 12th international conference on predictive models and data analytics in software engineering. ACM, p 9

  • Borges H, Hora A, Valente MT (2016b) Understanding the factors that impact the popularity of github repositories. In: 2016 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 334–344

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Cliff N (2014) Ordinal methods for behavioral data analysis. Psychology Press, New York, NY

    Book  Google Scholar 

  • Collberg C, Proebsting TA (2016) Repeatability in computer systems research. Commun ACM 59(3):62–69

    Article  Google Scholar 

  • Collobert R, Bengio S, Mariéthoz J (2002) Torch: a modular machine learning software library. Tech. rep., Idiap

  • Cutler A, Cutler DR, Stevens JR (2012) Random forests. In: Ensemble machine learning. Springer, pp 157–175

  • Fan Y, Xia X, Lo D, Hassan AE (2018a) Chaff from the wheat: characterizing and determining valid bug reports. In: IEEE transactions on software engineering

  • Fan Y, Xia X, Lo D, Li S (2018b) Early prediction of merged code changes to prioritize reviewing tasks. Empir Softw Eng 23(6):3346–3393

    Article  Google Scholar 

  • Fan Y, Xia X, da Costa DA, Lo D, Hassan AE, Li S (2019) The impact of changes mislabeled by szz on just-in-time defect prediction. In: IEEE transactions on software engineering

  • Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378

    Article  Google Scholar 

  • Fogel K (2005) Producing open source software: how to run a successful free software project. O’Reilly Media, Inc

  • Ghotra B, McIntosh S, Hassan AE (2015) Revisiting the impact of classification techniques on the performance of defect prediction models. In: 2015 IEEE/ACM 37th IEEE international conference on software engineering, vol 1. IEEE, pp 789–800

  • Gousios G, Pinzger M, Deursen AV (2014) An exploratory study of the pull-based software development model. In: Proceedings of the 36th international conference on software engineering. ACM, pp 345–355

  • Gundersen OE, Gil Y, Aha DW (2017) On reproducible ai: towards reproducible research, open science, and digital scholarship in ai publications. AI Mag 39(3):56–68

    Article  Google Scholar 

  • Han J, Deng S, Xia X, Wang D, Yin J (2019) Characterization and prediction of popular projects on github. In: 2019 IEEE 43rd annual computer software and applications conference (COMPSAC), vol 1. IEEE, pp 21–26

  • Harrell FE Jr (2015) Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer, Berlin

    Book  Google Scholar 

  • Hosmer DW Jr, Lemeshow S, Sturdiest RX (2013) Applied logistic regression, vol 398. Wiley, Hoboken

    Book  Google Scholar 

  • Hu Y, Zhang J, Bai X, Yu S, Yang Z (2016) Influence analysis of github repositories. SpringerPlus 5(1):1268

    Article  Google Scholar 

  • Huang J, Ling CX (2005) Using auc and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299–310

    Article  Google Scholar 

  • Jiang J, Lo D, He J, Xia X, Kochhar PS, Zhang L (2017) Why and how developers fork what from whom in github. Empir Softw Eng 22(1):547–578

    Article  Google Scholar 

  • Kim M, Bergman L, Lau T, Notkin D (2004) An ethnographic study of copy and paste programming practices in oopl. In: Proceedings. 2004 International symposium on empirical software engineering, ISESE’04. IEEE, pp 83–92

  • Kimble J (1992) Plain english: a charter for clear writing. TM Cooley L Rev 9:1

    Google Scholar 

  • Li Z, Lu S, Myagmar S, Zhou Y (2006) Cp-miner: finding copy-paste and related bugs in large-scale software code. IEEE Trans Softw Eng 32 (3):176–192

    Article  Google Scholar 

  • Newman D, Lau JH, Grieser K, Baldwin T (2010) Automatic evaluation of topic coherence. In: Human language technologies: the 2010 annual conference of the North American chapter of the association for computational linguistics, pp 100–108

  • Nosek BA, Alter G, Banks GC, Borsboom D, Bowman SD, Breckler SJ, Buck S, Chambers CD, Chin G, Christensen G, et al. (2015) Promoting an open research culture. Science 348(6242):1422–1425

    Article  Google Scholar 

  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al. (2019) Pytorch: an imperative style high-performance deep learning library. In: Advances in neural information processing systems, pp 8024–8035

  • Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explor Newsl 6(1):50–59

    Article  Google Scholar 

  • Portugal RLQ, do Prado Leite JCS (2016) Extracting requirements patterns from software repositories. In: 2016 IEEE 24th international requirements engineering conference workshops (REW). IEEE, pp 304–307

  • Prana GAA, Treude C, Thung F, Atapattu T, Lo D (2019) Categorizing the content of GitHub README files. Empir Softw Eng 24(3):1296–1327

    Article  Google Scholar 

  • Schober P, Boer C, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. Anesth Analg 126(5):1763–1768

    Article  Google Scholar 

  • Scott AJ, Knott M (1974) A cluster analysis method for grouping means in the analysis of variance. Biometrics 30(3):507–512

    Article  Google Scholar 

  • Sonnenburg S, Braun ML, Ong CS, Bengio S, Bottou L, Holmes G, LeCun Y, MÞller KR, Pereira F, Rasmussen CE, et al. (2007) The need for open source software in machine learning. J Mach Learn Res 8:2443–2466

    Google Scholar 

  • Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT Press

  • Tantithamthavorn C, Hassan AE (2018) An experience report on defect modelling in practice: pitfalls and challenges. In: Proceedings of the 40th international conference on software engineering: software engineering in practice. ACM, pp 286–295

  • Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2017) An empirical comparison of model validation techniques for defect prediction models. IEEE Trans Softw Eng 43(1):1–18

    Article  Google Scholar 

  • Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2018) The impact of automated parameter optimization on defect prediction models. IEEE Trans Softw Eng 45(7):683–711

    Article  Google Scholar 

  • Tian Y, Nagappan M, Lo D, Hassan AE (2015) What are the characteristics of high-rated apps? A case study on free android applications. In: 2015 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 301–310

  • Upton GJ (1992) Fisher’s exact test. J R Stat Soc: Ser A (Stat Soc) 155(3):395–402

    Article  Google Scholar 

  • Wan Z, Lo D, Xia X, Cai L, Li S (2017) Mining sandboxes for linux containers. In: IEEE international conference on software testing, verification and validation (ICST). IEEE, pp 92–102

  • Wan Z, Xia X, Hassan AE, Lo D, Yin J, Yang X (2018) Perceptions, expectations, and challenges in defect prediction. IEEE Trans Softw Eng 46(11):1241–1266

    Article  Google Scholar 

  • Wang TC, Liu MY, Zhu JY, Liu G, Tao A, Kautz J, Catanzaro B (2018) Video-to-video synthesis. In: Advances in neural information processing systems, vol 31, pp 1144–1156

  • Weber S, Luo J (2014) What makes an open source code popular on git hub?. In: IEEE international conference on data mining workshop. IEEE, pp 851–855

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83

    Article  Google Scholar 

  • Woodfield SN, Dunsmore HE, Shen VY (1981) The effect of modularization and comments on program comprehension. In: Proceedings of the 5th international conference on Software engineering. IEEE Press, pp 215–223

  • Xia X, Wan Z, Kochhar PS, Lo D (2019) How practitioners perceive coding proficiency. In: 2019 IEEE/ACM 41st international conference on software engineering (ICSE). IEEE, pp 924–935

  • Yan M, Xia X, Zhang X, Yang D, Xu L (2017) Automating aggregation for software quality modeling. In: IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 529–533

  • Yan M, Xia X, Shihab E, Lo D, Yin J, Yang X (2018) Automating change-level self-admitted technical debt determination. IEEE Trans Softw Eng 45(12):1211–1229

    Article  Google Scholar 

  • Yang J, Lu J, Lee S, Batra D, Parikh D (2018) Graph r-cnn for scene graph generation. In: Proceedings of the European conference on computer vision (ECCV, pp 670–685

  • Zar JH (2005) Spearman rank correlation. Encyclopedia of Biostatistics 7

  • Zhu J, Zhou M, Mockus A (2014) Patterns of folder use and project popularity: a case study of github repositories. In: Proceedings of the 8th ACM/IEEE international symposium on empirical software engineering and measurement. ACM, p 30

Download references

Acknowledgements

This research was partially supported by the National Key R&D Program of China (No. 2018YFB1003904) and the Australian Research Council’s Discovery Early Career Researcher Award (DECRA) (DE200100021).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Yuanrui Fan & Shanping Li

  2. PengCheng Laboratory, Shenzhen, China

    Yuanrui Fan

  3. Faculty of Information Technology, Monash University, Melbourne, Australia

    Xin Xia

  4. School of Information Systems, Singapore Management University, Singapore, Singapore

    David Lo

  5. School of Computing, Queen’s University, Kingston, Canada

    Ahmed E. Hassan

Authors
  1. Yuanrui Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Lo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed E. Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shanping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Xia.

Additional information

Communicated by: Tim Menzies

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, Y., Xia, X., Lo, D. et al. What makes a popular academic AI repository?. Empir Software Eng 26, 2 (2021). https://doi.org/10.1007/s10664-020-09916-6

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10664-020-09916-6

Keywords

Search

Navigation