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A software feature usually evolves over time: developers extend its first version as new use cases and requirements arrive, and each extension builds on code written earlier. Most feature benchmarks evaluate a single request on a fixed codebase. Feature evolution requires evaluating successive extensions on an agent's...
This initial study demonstrates that directly prompting off-the-shelf code LLMs remains ineffective; even advanced prompting strategies cannot compensate for the lack of task-specific knowledge, and fine-tuning proves highly effective, with pseudo-code representation consistently yielding the best performance.
A Translation fRAmework Via rule-guided reasoning and rEinforcement Learning, namely TRAVEL, consisting of two modules that employs Monte Carlo Tree Search-based reasoning path construction guided by Rust-specific rules, and introduces reinforcement learning that couples execution feedback with reasoning-quality signal...
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