12 citations · 23 across the 20 of their papers we have counts for
19 papers · 1 filter
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…
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…
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…
Automated File-Level Logging Generation for Machine Learning Applications using LLMs: A Case Study using GPT-4o Mini
Mayra Sofia Ruiz Rodriguez, SayedHassan Khatoonabadi, Emad Shihab
Logging is essential in software development, helping developers monitor system behavior and aiding in debugging applications. Given the ability of large language models (LLMs) to…
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…