activity
20242026
collaborators

14 papers

cs.SE2026

OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research

Musfiqur Rahman, SayedHassan Khatoonabadi, Emad Shihab

Existing class-level code generation datasets are either synthetic (ClassEval: 100 classes) or insufficient in scale for modern training needs (RealClassEval: 400 classes), hinderi…

cs.SE2026

Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source

Musfiqur Rahman, Emad Shihab

The integration of AI agents as coding assistants into software development has raised questions about the long-term viability of AI agent-generated code. A prevailing hypothesis w…

cs.SE2026

Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering

Mohamad Salim, Jasmine Latendresse, SayedHassan Khatoonabadi +1

LLM-based Multi-Agent (LLM-MA) systems are increasingly applied to automate complex software engineering tasks such as requirements engineering, code generation, and testing. Howev…

cs.SE2026

Evaluating the Use of LLMs for Automated DOM-Level Resolution of Web Performance Issues

Gideon Peters, SayedHassan Khatoonabadi, Emad Shihab

Users demand fast, seamless webpage experiences, yet developers often struggle to meet these expectations within tight constraints. Performance optimization, while critical, is a t…

cs.SE2025

Automatic Detection of LLM-Generated Code: A Comparative Case Study of Contemporary Models Across Function and Class Granularities

Musfiqur Rahman, SayedHassan Khatoonabadi, Ahmad Abdellatif +1

The adoption of Large Language Models (LLMs) for code generation risks incorporating vulnerable code into software systems. Existing detectors face two critical limitations: a lack…

cs.SE2025

The Impact of Large Language Models (LLMs) on Code Review Process

Antonio Collante, Samuel Abedu, SayedHassan Khatoonabadi +3

Large language models (LLMs) have recently gained prominence in the field of software development, significantly boosting productivity and simplifying teamwork. Although prior stud…