2 citations · 2 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
Beyond Synthetic Benchmarks: Evaluating LLM Performance on Real-World Class-Level Code Generation
Musfiqur Rahman, SayedHassan Khatoonabadi, Emad Shihab
Large language models (LLMs) have demonstrated strong performance on function-level code generation benchmarks, yet real-world software development increasingly demands class-level…
The Impact of Environment Configurations on the Stability of AI-Enabled Systems
Musfiqur Rahman, SayedHassan Khatoonabadi, Ahmad Abdellatif +2
Nowadays, software systems tend to include Artificial Intelligence (AI) components. Changes in the operational environment have been known to negatively impact the stability of AI-…