Data profiling is critical in machine learning for generating descriptive statistics, supporting both deeper understanding and downstream tasks like data valuation and curation. This work addresses profiling specifically in the context of code datasets for Large Language Models (code-LLMs), where data quality directly influences tasks such as code generation and summarization. Characterizing code datasets in terms of programming language concepts enables better insights and targeted data curation. Our proposed methodology decomposes code data profiling into two phases: (1) an offline phase where LLMs are leveraged to derive and learn rules for extracting syntactic and semantic concepts across various programming languages, including previously unseen or low-resource languages, and (2) an online deterministic phase applying these derived rules for efficient real-time analysis. This hybrid approach is customizable, extensible to new syntactic and semantic constructs, and scalable to multiple languages. Experimentally, our LLM-aided method achieves a mean accuracy of 90.33% for syntactic extraction rules and semantic classification accuracies averaging 80% and 77% across languages and semantic concepts, respectively.
View on arXiv@article{thorat2025_2503.15571,
title={ LLM-Aided Customizable Profiling of Code Data Based On Programming Language Concepts },
author={ Pankaj Thorat and Adnan Qidwai and Adrija Dhar and Aishwariya Chakraborty and Anand Eswaran and Hima Patel and Praveen Jayachandran },
journal={arXiv preprint arXiv:2503.15571},
year={ 2025 }
}