1 citations · 1 across the 10 of their papers we have counts for
32 papers · 1 filter
From Business Requirements to Test Assertions: Evaluating LLM-Generated Oracles on Real Bugs
Tiancheng Ma, Nasir U. Eisty
The oracle problem (determining the correct expected outcome for a test) remains a major bottleneck in automated testing, and is increasingly relevant as non-experts rely on AI-gen…
Self-Admitted Technical Debt in Scientific Software: Prioritization, Sentiment, and Propagation Across Artifacts
Eric L. Melin, Nasir U. Eisty, Gregory R. Watson +1
Self-admitted technical debt (SATD) impairs scientific software (SSW), yet its prioritization, sentiment, persistence, and propagation remains underexplored. Understanding how SSW…
SentTrack: Sentiment-Driven Bottleneck Detection in GitHub Issue Repositories
Xinyu Hu, Ali Behbahani, Daniel Moon +2
Software engineering teams increasingly depend on GitHub issue threads to coordinate work, report bugs, and negotiate technical decisions, yet most repository health tools focus on…
LLM vs. Human Unit Tests: Fault Detection on Real Python Bugs
Phouvadeth Vathana, Prapti Bhatt, Rishi Patel +1
Large language models (LLMs) have shown considerable promise for automated unit test generation, yet their practical effectiveness relative to human-written tests remains poorly un…
Exploring Sustainability in Scientific Software through Code Quality & Test Coverage Metrics
Sheikh Md. Mushfiqur Rahman, Gregory R. Watson, Nasir U. Eisty
Context: Scientific open-source software (SciOSS) plays a foundational role in research and engineering, yet its long-term sustainability has often been overlooked and remains a si…
Characterizing the Usefulness of Code Review Comments in Scientific Software for Software Quality and Scientific Rigor
Sharif Ahmed, Nasir U. Eisty
Context: Innovation thrives on scientific software, with useful code review feedback enhancing its correctness and impact. However, unlike general-purpose commercial and open-sourc…