12 citations · 23 across the 20 of their papers we have counts for
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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…
How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
Jasmine Latendresse, SayedHassan Khatoonabadi, Emad Shihab
Software libraries are central to the functionality, security, and maintainability of modern code. As developers increasingly turn to Large Language Models (LLMs) to assist with pr…
Evaluating the Use of LLMs for Documentation to Code Traceability
Ebube Alor, SayedHassan Khatoonabadi, Emad Shihab
Large Language Models (LLMs) offer new potential for automating documentation-to-code traceability, yet their capabilities remain underexplored. We present a comprehensive evaluati…
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…