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Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)

Toufique Ahmed, Kunal Suresh Pai, Premkumar T. Devanbu, Earl T. Barr

Abstract

Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineering. Researchers are still learning how to best "program" these LLMs to help developers. We start with the intuition that developers tend to consciously and unconsciously collect semantics facts, from the code, while working. Mostly these are shallow, simple facts arising from a quick read. For a function, such facts might include parameter and local variable names, return expressions, simple pre- and post-conditions, and basic control and data flow, etc.

BibTeX
@inproceedings{Ahmed-al:ICSE24,
  author    = {Toufique Ahmed and
               Kunal Suresh Pai and
               Premkumar T. Devanbu and
               Earl T. Barr},
  title     = {Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)},
  booktitle = {ICSE},
  pages     = {220:1--220:13},
  publisher = {{ACM}},
  year      = {2024},
}

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