ARGUS extracts fragmented code change rationales from multiple documents using LLMs and generates summaries that developers rate as useful for review and maintenance.
An empirical study on commit message generation using llms via in-context learning
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A systematic review that categorizes prompting strategies for LLM-based code summarization, assesses their effectiveness, and identifies gaps in research and evaluation practices.
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Fine-grained Multi-Document Extraction and Generation of Code Change Rationale
ARGUS extracts fragmented code change rationales from multiple documents using LLMs and generates summaries that developers rate as useful for review and maintenance.
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Prompt-Driven Code Summarization: A Systematic Literature Review
A systematic review that categorizes prompting strategies for LLM-based code summarization, assesses their effectiveness, and identifies gaps in research and evaluation practices.