LLM-based security code review is vulnerable to framing bias, with a novel iterative refinement attack achieving 100% success in reintroducing vulnerabilities across real projects.
Risk-driven online testing and test case diversity analysis for ml-enabled critical systems
4 Pith papers cite this work. Polarity classification is still indexing.
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KingsGuard adds hardware data-flow tracking and checks to TEE enclaves to prevent sensitive data leakage from vulnerabilities while supporting intentional declassification.
LightGBM with team-level features outperforms a bank's existing rule-based change risk process on a one-year dataset while using SHAP for regulatory explainability.
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.
citing papers explorer
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Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
LLM-based security code review is vulnerable to framing bias, with a novel iterative refinement attack achieving 100% success in reintroducing vulnerabilities across real projects.
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KingsGuard: Enclave Data Protection Under Real-World TEE Vulnerabilities
KingsGuard adds hardware data-flow tracking and checks to TEE enclaves to prevent sensitive data leakage from vulnerabilities while supporting intentional declassification.
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Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
LightGBM with team-level features outperforms a bank's existing rule-based change risk process on a one-year dataset while using SHAP for regulatory explainability.
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Software Engineering for Self-Adaptive Robotics: A Research Agenda
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.