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Found 6 papers, 1 papers with code
Calibration and Correctness of Language Models for Code
Our contributions will lead to better-calibrated decision-making in the current use of code generated by language models, and offers a framework for future research to further improve calibration methods for generative models in Software Engineering.
Towards Understanding What Code Language Models Learned
To understand the extent to which language models can learn some form of meaning, we investigate their ability to capture semantics of code beyond superficial frequency and co-occurrence.
Code to Comment "Translation": Data, Metrics, Baselining & Evaluation
We carefully examine the underlying assumption here: that the task of generating comments sufficiently resembles the task of translating between natural languages, and so similar models and evaluation metrics could be used.
Deep Learning & Software Engineering: State of Research and Future Directions
The intent of this report is to serve as a potential roadmap to guide future work that sits at the intersection of SE & DL.
Do People Prefer "Natural" code?
Natural code is known to be very repetitive (much more so than natural language corpora); furthermore, this repetitiveness persists, even after accounting for the simpler syntax of code.
Studying the Difference Between Natural and Programming Language Corpora
Code corpora, as observed in large software systems, are now known to be far more repetitive and predictable than natural language corpora.
