Prémkumar Dévanbu
4 papers in the PaperMetrix corpus
Papers by this author
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Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries
2023
Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult …
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Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code
2024 · arXiv (Cornell University)
Large language models for code (LLM4Code), which demonstrate strong performance (e.g., high accuracy) in processing source code, have significantly transformed software engineering. Many studies separately investigate the non-functional properties of LM4Code, but there is no …
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Are deep neural networks the best choice for modeling source code?
2017
Current statistical language modeling techniques, including deep-learning based models, have proven to be quite effective for source code. We argue here that the special properties of source code can be exploited for further improvements. In …
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Few-shot training LLMs for project-specific code-summarization
2022
Very large language models (LLMs), such as GPT-3 and Codex have achieved state-of-the-art performance on several natural-language tasks, and show great promise also for code. A particularly exciting aspect of LLMs is their knack for …