12 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…
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…
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…
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…
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…
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…