AI coding agents produce pull requests with substantially more commits and slightly higher description-to-diff similarity than human developers, based on analysis of 29,095 merged PRs.
Desmarais, and Giuliano Antoniol
3 Pith papers cite this work, alongside 44 external citations. Polarity classification is still indexing.
3
Pith papers citing it
44
external citations · OpenAlex
fields
cs.SE 3years
2026 3representative citing papers
Constrained decoding derived from OpenAPI specifications eliminates hallucinated web API calls in LLM-generated code and substantially improves correctness across 24 models, while retrieval-augmented generation yields inconsistent, model-dependent gains.
Prompt Coverage Adequacy, measured via attention boosting in LLMs, is associated with fault detection and uncovers over 30% more faults than traditional code coverage when guiding test generation across two datasets.
citing papers explorer
-
How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests
AI coding agents produce pull requests with substantially more commits and slightly higher description-to-diff similarity than human developers, based on analysis of 29,095 merged PRs.
-
Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding
Constrained decoding derived from OpenAPI specifications eliminates hallucinated web API calls in LLM-generated code and substantially improves correctness across 24 models, while retrieval-augmented generation yields inconsistent, model-dependent gains.
-
Prompt Coverage Adequacy
Prompt Coverage Adequacy, measured via attention boosting in LLMs, is associated with fault detection and uncovers over 30% more faults than traditional code coverage when guiding test generation across two datasets.