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Cited by 37

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Software provenance tracking at the scale of public source code

Rousseau, Guillaume, Di Cosmo, Roberto, Zacchiroli, Stefano·2020·Empirical Software Engineering
DOI 10.1007/s10664-020-09828-5
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Research works printed but not linked: 57

  1. Abramatic J-F, Di Cosmo R, Zacchiroli S (2018) Building the universal archive of source code. Commun ACM 61(10):29–31 · DOI
  2. Albert R, Barabási A (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47 · DOI · Paper page
  3. Alexandru CV, Panichella S, Gall HC (2017) Reducing redundancies in multi-revision code analysis. In: Pinzger M, Bavota G, Marcus A (eds) IEEE 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017, Klagenfurt, Austria, February 20-24, 2017, pp 148–159 · DOI
  4. Alexandru CV, Panichella S, Proksch S, Gall HC (2019) Redundancy-free analysis of multi-revision software artifacts. Empir Softw Eng 24(1):332–380 · DOI
  5. Allamanis M, Sutton CA (2013) Mining source code repositories at massive scale using language modeling. In: Zimmermann T, Di Penta M, Kim S (eds) Proceedings of the 10th working conference on mining software repositories, MSR ’13, San Francisco, CA, USA, May 18-19, 2013, pp 207–216. IEEE Computer Society · DOI
  6. Thomas J., Bergin T (2007) A history of the history of programming languages. Commun ACM 50(5):69–74 · DOI
  7. Biazzini M, Baudry B (2014) May the fork be with you: novel metrics to analyze collaboration on github. In: Proceedings of the 5th international workshop on emerging trends in software metrics, pp 37–43. ACM · DOI
  8. Borges H, Hora A, Valente MT (2016) Understanding the factors that impact the popularity of github repositories. In 2016 IEEE international conference on software maintenance and evolution (ICSME), pp 334–344 · DOI
  9. Brooks FP Jr (1978) The mythical man-month: essays on software engineering, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston
  10. Caneill M, Germȧn DM, Zacchiroli S (2017) The Debsources dataset: Two decades of free and open source software. Empir Softw Eng 22(3):1405–1437 · DOI
  11. Capraro M, Riehle D (2017) Inner source definition, benefits, and challenges. ACM Comput Surv (CSUR) 49(4):67 · DOI
  12. Crowston K, Wei K, Howison J, Wiggins A (2008) Free/libre open-source software development: What we know and what we do not know. ACM Comput Surv 44:27:1–7:35
  13. Davies J, Germȧn DM, Godfrey MW, Hindle A (2013) Software bertillonage - determining the provenance of software development artifacts. Empir Softw Eng 18 (6):1195–1237 · DOI
  14. Di Cosmo R, Zacchiroli S (2017) Software heritage: Why and how to preserve software source code. In: Proceedings of the 14th international conference on digital preservation, iPRES 2017, Kyoto, Japan. Available from https://hal.archives-ouvertes.fr/hal-01590958
  15. Dorogovtsev SN, Mendes JFF (2002) Evolution of networks. Adv Phys 51 (4):1079–1187 · DOI
  16. Dyer R, Nguyen HA, Rajan H, Nguyen TN (2013) Boa: A language and infrastructure for analyzing ultra-large-scale software repositories. In: Proceedings of the 2013 International Conference on Software Engineering, pp 422–431. IEEE Press · DOI
  17. Germán DM, Di Penta M, Guéhéneuc Y-G, Antoniol G (2009) Code siblings: Technical and legal implications of copying code between applications. In: Godfrey and Whitehead (Godfrey and Godfrey 2009), pp 81–90 · DOI
  18. Gkortzis A, Mitropoulos D, Spinellis D (2018) Vulinoss: A dataset of security vulnerabilities in open-source systems. In: Zaidman et al. (Zaidman et al 2018), pp 18–21 · DOI
  19. Godfrey MW (2015) Understanding software artifact provenance. Sci Comput Program 97:86–90 · DOI
  20. Godfrey MW, German DM, Davies J, Hindle A (2011) Determining the provenance of software artifacts. In: Proceedings of the 5th international workshop on software clones, IWSC ’11. ACM, New York, pp 65–66 · DOI
  21. Godfrey MW, Godfrey J (eds) (2009) Proceedings of the 6th international working conference on mining software repositories, MSR 2009 (Co-located with ICSE). Proceedings,. IEEE Computer Society, Vancouver
  22. Gousios G, Pinzger M, van Deursen A (2014) An exploratory study of the pull-based software development model. In: Proceedings of the 36th international conference on software engineering, pp 345–355. ACM · DOI · Paper page
  23. Grieco G, Luis Grinblat G, Uzal L, Rawat S, Feist J, Mounier L (2016) Toward large-scale vulnerability discovery using machine learning. In: Proceedings of the 6th ACM conference on data and application security and privacy, CODASPY ’16. ACM, New York, pp 85–96 · DOI
  24. Hassan AE (2008) The road ahead for mining software repositories. In: Frontiers of software maintenance FoSM 2008., pp 48–57. IEEE · DOI
  25. Hatton L, Spinellis D, van Genuchten M (2017) The long-term growth rate of evolving software: Empirical results and implications. Journal of Software: Evolution and Process, 29(5) · DOI
  26. Herraiz I, Rodríguez D, Robles G, Gonzȧlez-Barahona JM (2013) The evolution of the laws of software evolution: A discussion based on a systematic literature review. ACM Comput Surv 46(2):28:1–28:28 · DOI
  27. Ishio T, Kula RG, Kanda T, German DM, Inoue K (2016) Software ingredients: Detection of Third-Party component reuse in java software release. In: 2016 IEEE/ACM, 13th working conference on mining software repositories (MSR), pp 339–350 · DOI
  28. Jiang J, Lo D, He J, Xia X, Kochhar PS, Li Z (2017) Why and how developers fork what from whom in github. Empir Softw Eng 22(1):547–578 · DOI
  29. Lehman MM (1980) On understanding laws, evolution, and conservation in the large-program life cycle. J Syst Softw 1:213–221 · DOI
  30. Leskovec J, Sosič R (2016) Snap: A general-purpose network analysis and graph-mining library. ACM Trans Intell Syst Technol (TIST) 8(1):1 · DOI
  31. Li F, Paxson V (2017) A large-scale empirical study of security patches. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, CCS ’17. ACM, New York, pp 2201–2215 · DOI
  32. Lopes CV, Maj P, Martins P, Saini V, Yang D, Zitny J, Sajnani H, Vitek J (2017) Dėjȧvu: A map of code duplicates on github. PACMPL 1(OOPSLA) 28:1–84
  33. Ma Y, Bogart C, Amreen S, Zaretzki R, Mockus A (2019) World of code: an infrastructure for mining the universe of open source VCS data. In: Storey et al. (Storey et al 2019), pp 143–154 · DOI
  34. Markovtsev V, Long W (2018) Public git archive: A big code dataset for all. In: Zaidman et al. (Zaidman et al 2018), pp 34–37 · DOI
  35. Martinez M, Monperrus M (2015) Mining software repair models for reasoning on the search space of automated program fixing. Empir Softw Eng 20(1):176–205 · DOI
  36. Mockus A (2009) Amassing and indexing a large sample of version control systems: Towards the census of public source code history. In: Godfrey and Whitehead (Godfrey and Godfrey 2009), pp 11–20 · DOI
  37. Mockus A (2009) Amassing and indexing a large sample of version control systems: Towards the census of public source code history. In: Proceedings of the 2009 6th IEEE international working conference on mining software repositories, MSR ’09. IEEE Computer Society, Washington, pp 11–20 · DOI
  38. Newman M, Barabasi A-L, Watts DJ (2006) The structure and dynamics of networks: (Princeton studies in complexity). Princeton University Press, Princeton
  39. Pietri A, Spinellis D, Zacchiroli S (2019) The software heritage graph dataset: Public software development under one roof. In Storey et al. (Storey et al 2019), pp 138–142 · DOI
  40. Rastogi A, Nagappan N (2016) Forking and the sustainability of the developer community participation–an empirical investigation on outcomes and reasons. In: 2016 IEEE 23rd international conference on software analysis, evolution, and Reengineering (SANER), vol 1, pp 102–111. IEEE · DOI
  41. Rattan D, Bhatia R, Singh M (2013) Software clone detection: A systematic review. Inf Softw Technol 55(7):1165–1199 · DOI
  42. Semura Y, Yoshida N, Choi E, Inoue K (2017) Ccfindersw: Clone detection tool with flexible multilingual tokenizatio. In: Lv J, Zhang HJ, Hinchey M, Liu X (eds) 24th Asia-Pacific software engineering conference, APSEC 2017. IEEE Computer Society, Nanjing, pp 654–659 · DOI
  43. Spinellis D (2017) A repository of Unix history and evolution. Empir Softw Eng 22(3):1372–1404 · DOI
  44. Squire M (2017) The lives and deaths of open source code forges. In: Morgan L · DOI
  45. (ed) Proceedings of the 13th international symposium on open collaboration, OpenSym Galway, Ireland, August 23-25, 2017, pp 15:1-15:8. ACM
  46. Stol K-J, Fitzgerald B (2014) Inner source–adopting open source development practices in organizations: a tutorial. IEEE Softw 32(4):60–67 · DOI
  47. Storey M-AD, Adams B, Haiduc S (eds) (2019) Proceedings of the 16th international conference on mining software repositories, MSR 2019, 26-27. IEEE / ACM, Montreal
  48. Svajlenko J, Roy CK (2017) Fast and flexible large-scale clone detection with cloneworks. In: Uchitel S, Orso A, Robillard MP (eds) Proceedings of the 39th International Conference on Software Engineering, ICSE 2017, Buenos Aires, Argentina, May 20-28, 2017 - companion Volume, pp 27–30. IEEE Computer Society · DOI
  49. Thummalapenta S, Cerulo L, Aversano L, Di Penta M (2010) An empirical study on the maintenance of source code clones. Empir Softw Eng 15(1):1–34 · DOI
  50. Thung F, Bissyande TF, Lo D, Jiang L (2013) Network structure of social coding in github. In: 2013 17th European Conference on Software Maintenance and Reengineering, pp 323–326. IEEE · DOI
  51. Tiwari NM, Upadhyaya G, Rajan H (2016) Candoia: A platform and ecosystem for mining software repositories tools. In: Dillon LK, Visser W, Williams L (eds) Proceedings of the 38th international conference on software engineering, ICSE 2016, pp 759–764. ACM · DOI
  52. Tuunanen T, Koskinen Ji, Kärkkäinen T (2009) Automated software license analysis. Autom Softw Eng 16(3-4):455–490 · DOI
  53. Vendome C. (2015) A large scale study of license usage on github. In: 2015 IEEE/ACM 37th IEEE international conference on software engineering, vol 2, pp 772–774 · DOI
  54. Wu Y, Manabe Y, Kanda T, Germȧn DM, Inoue K (2017) Analysis of license inconsistency in large collections of open source projects. Empir Softw Eng 22 (3):1194–1222 · DOI
  55. Zaidman A, Kamei Y, Hill E (eds) (2018) Proceedings of the 15th International Conference on Mining Software Repositories, MSR 2018. ACM, Gothenburg
  56. Zimmermann T, Premraj R, Zeller A (2007) Predicting defects for eclipse. In: International workshop on predictor models in software engineering, 2007 PROMISE’07: ICSE Workshops 2007, pp 9–9 · DOI
  57. Zimmermann T, Weißgerber P, Diehl S, Zeller A (2004) Mining version histories to guide software changes. In: Finkelstein A, Estublier J, Rosenblum DS (eds) 26th international conference on software engineering (ICSE 2004), 23-28 May 2004, Edinburgh, pp 563–572 · DOI

Metadata

Authors
Rousseau, G., Di Cosmo, R., & Zacchiroli, S.
Venue
Empirical Software Engineering
Publisher
Springer Science and Business Media LLC
Published
2020-05-29
Volume
25
Issue
4
Pages
2930-2959
ISSN
  • 1382-3256
  • 1573-7616
DOI
10.1007/s10664-020-09828-5
Cited by
37
References
1
Language
en
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CoChat. (2026). Citation check: Software provenance tracking at the scale of public source code. https://app.cochat.ai/papers/10.1007/s10664-020-09828-5
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Rousseau, G., Di Cosmo, R., & Zacchiroli, S. (2020). Software provenance tracking at the scale of public source code. Empirical Software Engineering. https://doi.org/10.1007/s10664-020-09828-5

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