The paper delivers a taxonomy of seven LLM study types in software engineering along with eight guidelines that separate mandatory requirements from recommended practices to address reproducibility challenges.
arXiv preprint arXiv:2304.11085 (2023)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Relabeler is an end-to-end framework that detects corrupted labels via local and global instance relationships and corrects them using feature-based estimation, reporting up to 58% better label correction precision than baselines.
CANOLA estimates label noise and performs cautious iterative soft-label refinement to correct corrupted training data, reporting 19-52% error reduction versus prior methods on six datasets.
citing papers explorer
-
Guidelines for Empirical Studies in Software Engineering involving Large Language Models
The paper delivers a taxonomy of seven LLM study types in software engineering along with eight guidelines that separate mandatory requirements from recommended practices to address reproducibility challenges.
-
A Data-Centric Framework for Detecting and Correcting Corrupted Labels
Relabeler is an end-to-end framework that detects corrupted labels via local and global instance relationships and corrects them using feature-based estimation, reporting up to 58% better label correction precision than baselines.
-
Noise-Aware Framework for Correcting Corrupted Labels
CANOLA estimates label noise and performs cautious iterative soft-label refinement to correct corrupted training data, reporting 19-52% error reduction versus prior methods on six datasets.