# LLM-Aided Customizable Profiling of Code Data Based On Programming Language Concepts

arXiv: https://arxiv.org/abs/2503.15571 · HTML: https://mlsanity.com/paper/2503.15571/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Unified Pre-training for Program Understanding and Generation](https://mlsanity.com/paper/2103.06333/) (2021)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Synchromesh: Reliable code generation from pre-trained language models](https://mlsanity.com/paper/2201.11227/) (2022)
- [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://mlsanity.com/paper/2405.04324/) (2024)
- [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://mlsanity.com/paper/2304.09433/) (2023)
- [HoloClean: Holistic Data Repairs with Probabilistic Inference](https://mlsanity.com/paper/1702.00820/) (2017)
- [Code Prediction by Feeding Trees to Transformers](https://mlsanity.com/paper/2003.13848/) (2020)
- [DOBF: A Deobfuscation Pre-Training Objective for Programming Languages](https://mlsanity.com/paper/2102.07492/) (2021)
- [Language-Agnostic Representation Learning of Source Code from Structure and Context](https://mlsanity.com/paper/2103.11318/) (2021)
- [Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques](https://mlsanity.com/paper/2210.08258/) (2022)
- [Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning](https://mlsanity.com/paper/2407.05040/) (2024)
- [A Survey of Blocking and Filtering Techniques for Entity Resolution](https://mlsanity.com/paper/1905.06167/) (2019)
- [Learning to Extract Structured Entities Using Language Models](https://mlsanity.com/paper/2402.04437/) (2024)
- [Structure-aware Fine-tuning for Code Pre-trained Models](https://mlsanity.com/paper/2404.07471/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://mlsanity.com/paper/2510.23587/) (2025)
- [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://mlsanity.com/paper/2601.17058/) (2026)

## Abstract (arXiv)

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.