14 citations · 54 across the 49 of their papers we have counts for
13 papers · 1 filter
Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models
Eric L. Melin, Adam J. Torek, Nasir U. Eisty +1
Context: Large Language Models (LLMs) like GPT-5 and LLaMA-405b exhibit advanced code generation abilities, but their deployment demands substantial computation resources and energ…
Sentiment Analysis of ML Projects: Bridging Emotional Intelligence and Code Quality
Md Shoaib Ahmed, Dongyoung Park, Nasir U. Eisty
This study explores the intricate relationship between sentiment analysis (SA) and code quality within machine learning (ML) projects, illustrating how the emotional dynamics of de…
Exploring Requirements Elicitation from App Store User Reviews Using Large Language Models
Tanmai Kumar Ghosh, Atharva Pargaonkar, Nasir U. Eisty
Mobile applications have become indispensable companions in our daily lives. Spanning over the categories from communication and entertainment to healthcare and finance, these appl…
Code Vulnerability Detection: A Comparative Analysis of Emerging Large Language Models
Shaznin Sultana, Sadia Afreen, Nasir U. Eisty
The growing trend of vulnerability issues in software development as a result of a large dependence on open-source projects has received considerable attention recently. This paper…
Leveraging Large Language Models for Predicting Cost and Duration in Software Engineering Projects
Justin Carpenter, Chia-Ying Wu, Nasir U. Eisty
Accurate estimation of project costs and durations remains a pivotal challenge in software engineering, directly impacting budgeting and resource management. Traditional estimation…
Exploring the Advances in Using Machine Learning to Identify Technical Debt and Self-Admitted Technical Debt
Eric L. Melin, Nasir U. Eisty
In software engineering, technical debt, signifying the compromise between short-term expediency and long-term maintainability, is being addressed by researchers through various ma…
Introducing Ensemble Machine Learning Algorithms for Automatic Test Case Generation using Learning Based Testing
Sheikh Md. Mushfiqur Rahman, Nasir U. Eisty
Ensemble methods are powerful machine learning algorithms that combine multiple models to enhance prediction capabilities and reduce generalization errors. However, their potential…
The Kubernetes Security Landscape: AI-Driven Insights from Developer Discussions
J. Alexander Curtis, Nasir U. Eisty
Kubernetes, the go-to container orchestration solution, has swiftly become the industry standard for managing containers at scale in production environments. Its widespread adoptio…
PVAC: Package Version Activity Categorizer, Leveraging Semantic Versioning in a Heterogeneous System
Shane K. Panter, Luke Hindman, Nasir U. Eisty
Context: Modern open-source software ecosystems, such as those managed by GNU/Linux distributions, are composed of numerous packages developed independently by diverse communities.…
Rusty Linux: Advances in Rust for Linux Kernel Development
Shane K. Panter, Nasir U. Eisty
Context: The integration of Rust into kernel development is a transformative endeavor aimed at enhancing system security and reliability by leveraging Rust's strong memory safety g…
Decade-long Utilization Patterns of ICSE Technical Papers and Associated Artifacts
Sharif Ahmed, Rey Ortiz, Nasir U. Eisty
Context: Annually, ICSE acknowledges a range of papers, a subset of which are paired with research artifacts such as source code, datasets, and supplementary materials, adhering to…
Exploiting CPU Clock Modulation for Covert Communication Channel
Shariful Alam, Jidong Xiao, Nasir U. Eisty
Covert channel attacks represent a significant threat to system security, leveraging shared resources to clandestinely transmit information from highly secure systems, thereby viol…
Understanding Emojis :) in Useful Code Review Comments
Sharif Ahmed, Nasir U. Eisty
Emojis and emoticons serve as non-verbal cues and are increasingly prevalent across various platforms, including Modern Code Review. These cues often carry emotive or instructive w…