First empirical study of correctness bugs in torch.compile characterizes their patterns and proposes AlignGuard, which found 23 confirmed new bugs via LLM-guided test mutation.
What to blame? on the granularity of fault localization for deep neural networks
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JunoBench is the first benchmark of 111 reproducible crashes in Python ML Jupyter notebooks from Kaggle, with verified fixes and rich annotations for bug research.
RedShell fine-tunes LLMs on enhanced malicious PowerShell data to produce syntactically valid offensive code for pentesting, reporting over 90% validity, strong semantic match to references, and better edit-distance similarity than prior methods plus functional execution success.
MNAL reduces human effort in bug report labeling by up to 95.8% for readability and 196% for identifiability while improving identification performance and working with various neural models.
A research roadmap analyzing the current state of search-based software engineering with foundation models, outlining challenges and directions across three integration aspects.
RedShell fine-tunes LLMs on a custom dataset of public code samples to generate syntactically valid PowerShell scripts with semantic similarity to references, reporting under 10% parse errors and over 50%/40% mean similarity on Edit Distance and METEOR.
citing papers explorer
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Demystifying the Silence of Correctness Bugs in PyTorch Compiler
First empirical study of correctness bugs in torch.compile characterizes their patterns and proposes AlignGuard, which found 23 confirmed new bugs via LLM-guided test mutation.
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JunoBench: A Benchmark Dataset of Crashes in Python Machine Learning Jupyter Notebooks
JunoBench is the first benchmark of 111 reproducible crashes in Python ML Jupyter notebooks from Kaggle, with verified fixes and rich annotations for bug research.
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Towards Automated Pentesting with Large Language Models
RedShell fine-tunes LLMs on enhanced malicious PowerShell data to produce syntactically valid offensive code for pentesting, reporting over 90% validity, strong semantic match to references, and better edit-distance similarity than prior methods plus functional execution success.
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Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning
MNAL reduces human effort in bug report labeling by up to 95.8% for readability and 196% for identifiability while improving identification performance and working with various neural models.
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Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
A research roadmap analyzing the current state of search-based software engineering with foundation models, outlining challenges and directions across three integration aspects.
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RedShell: A Generative AI-Based Approach to Ethical Hacking
RedShell fine-tunes LLMs on a custom dataset of public code samples to generate syntactically valid PowerShell scripts with semantic similarity to references, reporting under 10% parse errors and over 50%/40% mean similarity on Edit Distance and METEOR.