most citedAssessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models

1 citations · 2 across the 5 of their papers we have counts for

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cs.SE2025

An Analysis of LLM Fine-Tuning and Few-Shot Learning for Flaky Test Detection and Classification

Riddhi More, Jeremy S. Bradbury

Flaky tests exhibit non-deterministic behavior during execution and they may pass or fail without any changes to the program under test. Detecting and classifying these flaky tests…

cs.SE2025★ 1 cited

Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models

Riddhi More, Jeremy S. Bradbury

Data augmentation has become a standard practice in software engineering to address limited or imbalanced data sets, particularly in specialized domains like test classification an…

cs.SE2024

Addressing Data Leakage in HumanEval Using Combinatorial Test Design

Jeremy S. Bradbury, Riddhi More

The use of large language models (LLMs) is widespread across many domains, including Software Engineering, where they have been used to automate tasks such as program generation an…