14 citations · 54 across the 49 of their papers we have counts for
50 papers
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,…
The Role of AI in Modern Penetration Testing
J. Alexander Curtis, Nasir U. Eisty
Penetration testing is a cornerstone of cybersecurity, traditionally driven by manual, time-intensive processes. As systems grow in complexity, there is a pressing need for more sc…
A survey of generative AI adoption and perceived productivity among scientists who program
Gabrielle O'Brien, Alexis Parker, Nasir Eisty +1
Programming is essential to modern scientific research, yet most scientists report inadequate training for the software development their work demands. Generative AI tools capable…
Exploring Scientific Debt: Harnessing AI for SATD Identification in Scientific Software
Eric L. Melin, Ahmed Musa Awon, Nasir U. Eisty +2
Developers often leave behind clues in their code, admitting where it falls short, known as Self-Admitted Technical Debt (SATD). In the world of Scientific Software (SSW), where in…
Peer Code Review in Research Software Development: The Research Software Engineer Perspective
Md Ariful Islam Malik, Jeffrey C. Carver, Nasir U. Eisty
Background: Research software is crucial for enabling research discoveries and supporting data analysis, simulation, and interpretation across domains. However, evolving requiremen…
Uncovering Scientific Software Sustainability through Community Engagement and Software Quality Metrics
Sharif Ahmed, Addi Malviya Thakur, Gregory R. Watson +1
Scientific open-source software (Sci-OSS) projects are critical for advancing research, yet sustaining these projects long-term remains a major challenge. This paper explores the s…
Deciphering WONTFIX: A Mixed-Method Study on Why GitHub Issues Get Rejected
J. Alexander Curtis, Sharadha Kasiviswanathan, Nasir Eisty
Context: The ``wontfix'' label is a widely used yet narrowly understood tool in GitHub repositories, indicating that an issue will not be pursued further. Despite its prevalence, t…
Beyond Pass/Fail: The Story of Learning-Based Testing
Sheikh Md. Mushfiqur Rahman, Nasir Eisty
Learning-Based Testing (LBT) merges learning and testing processes to achieve both testing and behavioral adequacy. LBT utilizes active learning to infer the model of the System Un…
Learning-Based Testing for Deep Learning: Enhancing Model Robustness with Adversarial Input Prioritization
Sheikh Md Mushfiqur Rahman, Nasir Eisty
Context: Deep Neural Networks (DNNs) are increasingly deployed in critical applications, where resilience against adversarial inputs is paramount. However, whether coverage-based o…
Ten Simple Rules for Catalyzing Collaborations and Building Bridges between Research Software Engineers and Software Engineering Researchers
Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon +3
In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pi…
Shaky Structures: The Wobbly World of Causal Graphs in Software Analytics
Jeremy Hulse, Nasir U. Eisty, Tim Menzies
Causal graphs are widely used in software engineering to document and explore causal relationships. Though widely used, they may also be wildly misleading. Causal structures genera…