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Recent language models have demonstrated proficiency in summarizing source code.
srcml: An infrastructure for the exploration, analysis, and manipulation of source code: A tool demonstration
Collard, M. L., Decker, M. J., and Maletic, J. I · 2013
Earlier work this paper cites.
An empirical study on the patterns of eye movement during summarization tasks
Rodeghero, P., and McMillan, C · 2015
Earlier work this paper cites.
Eye-tracking metrics in software engineering
Sharafi, Z., Shaffer, T., Sharif, B., and Guéhéneuc, Y.-G · 2015
Earlier work this paper cites.
Improving sentence compression by learning to predict gaze
Klerke, S., Goldberg, Y., and Søgaard, A · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M., and Lee, S.-I · 2017
Earlier work this paper cites.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Sequence classification with human attention
Barrett, M., Bingel, J., Hollenstein, N., Rei, M., and Søgaard, A · 2018
Earlier work this paper cites.
Using developer eye movements to externalize the mental model used in code summarization tasks
Abid, N. J., Maletic, J. I., and Sharif, B · 2019
Earlier work this paper cites.
Recommendations for datasets for source code summarization
LeClair, A., and McMillan, C · 2019
Earlier work this paper cites.
Using human attention to extract keyphrase from microblog post
Zhang, Y., and Zhang, C · 2019
Cited alongside, same era.
A transformer-based approach for source code summarization
Ahmad, W. U., Chakraborty, S., Ray, B., and Chang, K.-W · 2020
Cited alongside, same era.
Attention in natural language processing
Galassi, A., Lippi, M., and Torroni, P · 2020
Cited alongside, same era.
Interpretable machine learning
Molnar, C · 2020
Cited alongside, same era.
Improving natural language processing tasks with human gaze-guided neural attention
Sood, E., Tannert, S., Müller, P., and Bulling, A · 2020
Cited alongside, same era.
Thinking like a developer? comparing the attention of humans with neural models of code
Paltenghi, M., and Pradel, M · 2021
What does transformer learn about source code?
Zhang, K., Li, G., and Jin, Z · 2022
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Diet code is healthy: Simplifying programs for pre-trained models of code
Zhang, Z., Zhang, H., Shen, B., and Gu, X · 2022
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Tobii pro fusion user manual, Jun 2023
2023
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Modeling programmer attention as scanpath prediction
Bansal, A., Su, C.-Y., Karas, Z., Zhang, Y., Huang, Y., Li, T. J.-J., and McMillan, C · 2023
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Where to look when repairing code? comparing the attention of neural models and developers
Huber, D., Paltenghi, M., and Pradel, M · 2023
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Cited alongside, same era.
Wheacha: A method for explaining the predictions of code summarization models
Wang, Y., Wang, K., and Wang, L · 2021
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C · 2022
Cited alongside, same era.
Paltenghi, M., Pandita, R., Henley, A. Z., and Ziegler, A · 2022
Cited alongside, same era.
A systematic evaluation of large language models of code
Xu, F. F., Alon, U., Neubig, G., and Hellendoorn, V. J · 2022
Cited alongside, same era.
An extensive study on pre-trained models for program understanding and generation
Zeng, Z., Tan, H., Zhang, H., Li, J., Zhang, Y., and Zhang, L · 2022
Cited alongside, same era.
Evaluating feature importance estimates
Hooker, S., Erhan, D., Kindermans, P.-J., and Kim, B
Cited in the paper.
Kou, B., Chen, S., Wang, Z., Ma, L., and Zhang, T · 2023
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Starcoder: may the source be with you!
Li, R., Allal, L. B., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., Akiki, C., Li, J., Chim, J., et al · 2023
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On the reliability and explainability of automated code generation approaches
Liu, Y., Tantithamthavorn, C., Liu, Y., and Li, L · 2023
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Introducing chatgpt
OpenAI · 2023
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Gpt-4 technical report. arxiv 2303.08774
OpenAI, R · 2023
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Code llama: Open foundation models for code
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al · 2023
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