From the 1 of 9 linked papers with an AI index.
9 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…
CoCoMUT: A Tool for Code-Context Mining and Automated Dataset Generation
Alessandro Botta, Shiven Garisa, Jaya Vardhini Akurathi +3
Software-engineering assistants often need method-level context beyond an isolated body, including enclosing-class information, documentation, callers, callees, type hierarchy, and…
PITMuS: A Tool for Automated Bug Dataset Generation via Source-Level Mutant Reconstruction
Tasfia Tasnim, Soneya Binta Hossain
Mutation testing provides a scalable mechanism for generating controlled faults for software testing and empirical software engineering. In Java, PIT is a widely used mutation test…
From Threads to Trajectories: A Multi-LLM Pipeline for Community Knowledge Extraction from GitHub Issue Discussions
Nazia Shehnaz Joynab, Soneya Binta Hossain
Resolution of complex post-production issues in large-scale open-source software (OSS) projects requires significant cognitive effort, as developers need to go through long, unstru…
Measuring LLM Trust Allocation Across Conflicting Software Artifacts
Noshin Ulfat, Ahsanul Ameen Sabit, Soneya Binta Hossain
LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even when those artifacts are incomplete or mu…