Byam: Fixing Breaking Dependency Updates with Large Language Models

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Does developer familiarity hasten bug resolution? A causal inference perspective

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Robustness evaluation and enhancement of LLMs in code generation: an empirical study

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Evaluating large language models for multilingual vulnerability detection at dual granularities

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ArkTS code generation: A comprehensive evaluation with large language models

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Developers and generative AI: A study of self-admitted usage in open source projects

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How challenging it is to identify real code authors: an empirical study

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CPGHunter: LLM-guided semantic modeling for scalable vulnerability detection via taint analysis

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Prompt engineering in LLMs for automated unit test generation: A large-scale study

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HAFix: history-augmented large language models for bug fixing

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Evaluating AI-supported eliminative argumentation for developing reliable assurance cases

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Predicting Post-release Defects with Knowledge Units (KUs) of Programming Languages: An Empirical Study

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On the impact of black-box deployment strategies for edge AI on latency and model performance

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ContractFull: a rapid and comprehensive static analysis tool for Ethereum smart contracts

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An empirical study of testing practices in open source AI agent frameworks and agentic applications

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Less is more: usefulness of data flow diagrams and large language models for security threat validation

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AdvGen-X: Transferability driven adversarial example generation for pre-trained models of code

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Decoupling in AI ethics: Learning how to walk the talk

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An exploratory eye tracking study on how developers classify and debug Python code in different paradigms

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Implicit security requirements classification with large language models using the OWASP application security verification standard: a shift-left approach

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