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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SecureForge audits LLM code for vulnerabilities, builds a synthetic prompt corpus via Markovian sampling, and optimizes system prompts to cut security issues by up to 48% while preserving unit test performance, with zero-shot transfer to real prompts.
DynamicsLLM uses LLMs to generate execution traces that cover three times more code smell-related events than the prior Dynamics tool on 333 F-Droid Android apps, with a hybrid method adding 25.9% coverage for low-activity apps.
QTyBERT matches or exceeds BERT-based log anomaly detection effectiveness while reducing embedding generation time to near static word embedding levels.
STAF applies sentence embeddings from transformers to classify SCA findings, reaching 89% F1 and beating prior filters by 11% within projects and 6% across projects.
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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SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization
SecureForge audits LLM code for vulnerabilities, builds a synthetic prompt corpus via Markovian sampling, and optimizes system prompts to cut security issues by up to 48% while preserving unit test performance, with zero-shot transfer to real prompts.
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DynamicsLLM: a Dynamic Analysis-based Tool for Generating Intelligent Execution Traces Using LLMs to Detect Android Behavioural Code Smells
DynamicsLLM uses LLMs to generate execution traces that cover three times more code smell-related events than the prior Dynamics tool on 333 F-Droid Android apps, with a hybrid method adding 25.9% coverage for low-activity apps.
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A Comparative Study of Semantic Log Representations for Software Log-based Anomaly Detection
QTyBERT matches or exceeds BERT-based log anomaly detection effectiveness while reducing embedding generation time to near static word embedding levels.
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Towards Better Static Code Analysis Reports: Sentence Transformer-based Filtering of Non-Actionable Alerts
STAF applies sentence embeddings from transformers to classify SCA findings, reaching 89% F1 and beating prior filters by 11% within projects and 6% across projects.