From the 1 of 5 linked papers with an AI index.
5 papers
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…
PROGRESS: Property-Guided Regression Search for Semantic Falsification
Davis Tocheuk Mo, Noshin Ulfat, Matthew B. Dwyer +1
PROGRESS combines property‑based testing with coverage‑guided, search‑based regression test generation to automatically create tests that reach deep program states and expose bugs…
Latent Anchor-Driven Test Generation for Deep Neural Networks
Bin Duan, Matthew B. Dwyer, Guowei Yang
Deep Neural Networks (DNNs) are increasingly being deployed in security-critical and safety-sensitive applications, which makes rigorous testing essential to identify and mitigate…
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…
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…