Defines documentation-to-code equivalence and introduces Documentary to generate matching docs for 53.4% of function snippets, raising LLM output prediction accuracy by 12.8-24.5% over human-written docs.
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Bash-Commenter applies CPT, SFT, and Syntax-Aware Preference Optimization (SAPO) via AST atomic operations to LLaMA-3.1-8B, reporting higher BLEU-4/METEOR/ROUGE-L scores than baselines on single-line and multi-line Bash comment generation tasks.
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Natural Language-Focused Software Engineering via Code-Documentation Equivalence
Defines documentation-to-code equivalence and introduces Documentary to generate matching docs for 53.4% of function snippets, raising LLM output prediction accuracy by 12.8-24.5% over human-written docs.
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Bash-Commenter: Leveraging Syntax-Aware Preference Optimization to Reinforce Large Language Model for Bash Code Comment Generation
Bash-Commenter applies CPT, SFT, and Syntax-Aware Preference Optimization (SAPO) via AST atomic operations to LLaMA-3.1-8B, reporting higher BLEU-4/METEOR/ROUGE-L scores than baselines on single-line and multi-line Bash comment generation tasks.