CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code-Reference-Cited by-同舟云学术

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CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code

Published:2024-04-15 Issue: Volume: Page:637-641
ISSN:
Container-title:Proceedings of the 21st International Conference on Mining Software Repositories
language:
Short-container-title:

Author:

Weyssow Martin¹^ORCID,Di Sipio Claudio²^ORCID,Di Ruscio Davide²^ORCID,Sahraoui Houari¹^ORCID

Affiliation:

1. DIRO, University of Montreal, Montreal, Canada

2. University of l'Aquila, L'Aquila, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3643991.3644864

Reference28 articles.

1. Hussein Alrubaye Deema AlShoaibi Mohamed Wiem Mkaouer and Ali Ouni. [n. d.]. How Does API Migration Impact Software Quality and Comprehension? An Empirical Study. ([n. d.]) 12.

2. APIDiff: Detecting API breaking changes

3. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021).

4. Zimin Chen and Martin Monperrus. 2018. The codrep machine learning on source code competition. arXiv preprint arXiv:1807.03200 (2018).

5. Roberto Di Cosmo and Stefano Zacchiroli. 2017. Software Heritage: Why and How to Preserve Software Source Code. In International Conference on Digital Preservation. https://api.semanticscholar.org/CorpusID:53597098

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