Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization-Reference-Cited by-同舟云学术

Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization

Published:2024-04-12 Issue: Volume:45 Page:1-13
ISSN:
Container-title:Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
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Author:

Mastropaolo Antonio¹^ORCID,Ciniselli Matteo¹^ORCID,Di Penta Massimiliano²^ORCID,Bavota Gabriele¹^ORCID

Affiliation:

1. SEART (Software Analytics Research Team), Universita della Svizzera Italiana, Lugano, Switzerland

2. Department of Engineering, University of Sannio, Benevento, Italy

Funder

European Research Council

Italian ministry of research

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3639174

Reference97 articles.

1. [n. d.]. CC-News Dataset. https://commoncrawl.org/2016/10/news-dataset-available/.

2. [n. d.]. Replication Package https://github.com/antonio-mastropaolo/code-summarization-metric/tree/main.

3. [n. d.]. Stories Dataset. https://github.com/tensorflow/models/tree/archive/research/lm_commonsense#1-download-data-files.

4. Automated Documentation of Android Apps;Aghajani Emad;IEEE Transactions on Software Engineering,2019

5. Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021. Unified Pre-training for Program Understanding and Generation. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT. 2655--2668.

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