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Revisiting code readability improvement with LLMs: A critical assessment of fine-tuned models
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Software dependencies 2.0: An empirical study of reuse and integration of pre-trained models in open-source projects
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Meta-enhanced code: leveraging structural and functional features for precise cross-modal code search
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Empirical benchmarking of large language models for data science coding: a multidimensional evaluation
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A multi-language perspective on the robustness of LLM code generation
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CDBench: Benchmarking the mutation testing capabilities of LLMs with code defenders
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Is your prompt poisoning code? Defect induction rates and security mitigation strategies
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Balancing usefulness and naturalness: an LLM-based curation pipeline for code review comments
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Do influence tactics matter? investigating prompt framing effects in LLM code generation
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The transformative potential of AI in software engineering: a case study on LeetCode and ChatGPT
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MinsC2Rust: LLM-driven project-level code migration from C to safe Rust
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Linux Kernel 7.1 Officially Released with New NTFS Driver, Intel FRED, and Major Code Cleanup
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Smelly-shot is all you need: an empirical study to compare in-context learning verses fine tuning for code smell detection
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Exploring the capabilities of vision-language models to detect visual bugs in HTML5 applications
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From brittle to robust: Improving LLM annotations for SE optimization
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Linux Kernel 7.1.4 Released with Bug Fixes, Security Updates, and Hardware Improvements
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KDE Plasma 6.7.1 Released with Stability Fixes, UI Improvements, and Better Wayland Reliability
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Firefox 153 Released with HDR Video, Smarter PDF Tools, Better Privacy, and New Linux Improvements
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Large language models in model-driven engineering: a systematic mapping study
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Less is more: balancing models performance and complexity for software defects prediction
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Evaluating foundation model integration strategies for detecting PII in java software engineering pipelines
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The impact of critique on LLM-based model generation from natural language: the case of activity diagrams
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Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification
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An empirical evaluation of white-box and black-box test case prioritization techniques in CPSs modeled in Simulink
