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Found 4 papers, 3 papers with code
Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code
Therefore, auditing code developed using LLMs is challenging, as it is difficult to reliably assert if an LLM used during development has been trained on specific copyrighted codes, given that we do not have access to the training datasets of these models.
An Empirical Study on Bugs Inside PyTorch: A Replication Study
Software systems are increasingly relying on deep learning components, due to their remarkable capability of identifying complex data patterns and powering intelligent behaviour.
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
This allows us to compare different mutation killing definitions based on existing approaches, as well as to analyze the behavior of the obtained mutation operators and their potential combinations called Higher Order Mutation(s) (HOM).
GitHub Copilot AI pair programmer: Asset or Liability?
In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks.
