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20232026
most citedTOGLL: Correct and Strong Test Oracle Generation with LLMs

20 citations · 21 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.SE2026

Documentation vs. Code Patterns: What Drives LLM-Based Exception Oracle Generation?

Soneya Binta Hossain, Matthew B. Dwyer, Tasfia Tasnim

LLM-based test oracle generation (TOG) methods report high accuracy on exception oracle generation, but it remains unclear what evidence drives these predictions. In particular, do…

cs.SE2026

PROGRESS: Property-Guided Regression Search for Semantic Falsification

Davis Tocheuk Mo, Noshin Ulfat, Matthew B. Dwyer +1

Search-based regression-test generation effectively explores complex program structures, yielding high structural coverage, but its oracles are derived from the system under test:…

cs.SE2024

Doc2OracLL: Investigating the Impact of Documentation on LLM-based Test Oracle Generation

Soneya Binta Hossain, Raygan Taylor, Matthew Dwyer

Code documentation is a critical aspect of software development, serving as a bridge between human understanding and machine-readable code. Beyond assisting developers in understan…

cs.SE2024★ 20 cited

TOGLL: Correct and Strong Test Oracle Generation with LLMs

Soneya Binta Hossain, Matthew Dwyer

Test oracles play a crucial role in software testing, enabling effective bug detection. Despite initial promise, neural-based methods for automated test oracle generation often res…

cs.SE2023★ 1 cited

Artifact: Measuring and Mitigating Gaps in Structural Testing

Soneya Binta Hossain, Matthew B. Dwyer, Sebastian Elbaum +1

The artifact used for evaluating the experimental results of Measuring and Mitigating Gaps in Structural Testing is publicly available on GitHub, Software Heritage and figshare, an…