14 citations · 54 across the 49 of their papers we have counts for
14 papers · 1 filter
How Researchers Use and Verify AI Coding Assistants: Tasks and Validation Practices in Scientific Programming
Gabrielle O'Brien, Reed Milewicz, Nasir Eisty
Generative AI has entered research programming, yet there is little evidence about which tasks researchers hand to it or how they decide whether its code is correct. We draw on 527…
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…
Thinking Out Loud: Real-Time Deception Monitoring in Asymmetric LLM Negotiations
Nolan Coffey, Faithful Odoi, Makenzie Johnson +1
As LLM-based agents are increasingly deployed to negotiate, delegate, or transact on a user's behalf, software pipelines need runtime mechanisms to verify that an agent's stated in…
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…
Applications of Causality in Software Testing: A Rapid Review
Tiancheng Ma, Nasir U. Eisty
Causal inference offers a principled framework for understanding how interventions influence software behavior, yet its adoption in software testing remains fragmented across diffe…
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…
MALTA: Maintenance-Aware Technical Lag, Estimation to Address Software Abandonment
Shane K. Panter, Nasir U. Eisty
Context: Open-source ecosystems rely on sustained package maintenance. When maintenance slows or stops, Technical Lag (TL), the gap between installed and latest dependency versions…
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…
Technical Lag as Latent Technical Debt: A Rapid Review
Shane K. Panter, Nasir U. Eisty
Context: Technical lag accumulates when software systems fail to keep pace with technological advancements, leading to a deterioration in software quality. Objective: This paper ai…
Multi-Artifact Analysis of Self-Admitted Technical Debt in Scientific Software
Eric L. Melin, Nasir U. Eisty, Gregory Watson +1
Context: Self-admitted technical debt (SATD) occurs when developers acknowledge shortcuts in code. In scientific software (SSW), such debt poses unique risks to the validity and re…
Integrating APK Image and Text Data for Enhanced Threat Detection: A Multimodal Deep Learning Approach to Android Malware
Md Mashrur Arifin, Maqsudur Rahman, Nasir U. Eisty
As zero-day Android malware attacks grow more sophisticated, recent research highlights the effectiveness of using image-based representations of malware bytecode to detect previou…
LLMs in Code Vulnerability Analysis: A Proof of Concept
Shaznin Sultana, Sadia Afreen, Nasir U. Eisty
Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring intelligent automation to detect, assess,…