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In the rapidly evolving domain of software engineering (SE), Large Language Models (LLMs) are increasingly leveraged to automate developer support. Open source LLMs have grown competitive with pro- prietary models such as GPT-4 and Claude-3, without the associated financial and accessibility constraints. This study investigates whether state of the art open source LLMs including Solar-10.7B, CodeLlama-7B, Mistral-7B, Qwen2-7B, StarCoder2-7B, and LLaMA3-8B can generate responses to technical queries that align with those crafted by human experts. Leveraging retrieval augmented generation (RAG) and targeted fine tuning, we evaluate these models across critical performance dimen- sions, such as semantic alignment and contextual fluency. Our results show that Solar-10.7B, particularly when paired with RAG and fine tun- ing, most closely replicates expert level responses, o!ering a scalable and cost e!ective alternative to commercial models. This vision paper high- lights the potential of open-source LLMs to enable robust and accessible AI-powered developer assistance in software engineering. 

The Rust programming language is a prominent candidate for a C and C++ replacement in the memory-safe era. However, Rust’s safety guarantees do not in general extend to arbitrary third-party code. The main purpose of this short paper is to point out that this is true even entirely within safe Rust – which we illustrate through a series of counterexamples. To complement our examples, we present initial experimental results to investigate: do existing program analysis and program veri!cation tools detect or mitigate these risks? Are these attack patterns realizable via input to publicly exposed functions in real-world Rust libraries? And to what extent do existing supply chain attacks in Rust leverage similar attacks? All of our examples and associated data are available as an open source repository on GitHub. We hope this paper will inspire future work on rethinking safety in Rust – especially, to go beyond the safe/unsafe distinction and harden Rust against a stronger threat model of attacks that can be used in the wild.