# Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates

arXiv: https://arxiv.org/abs/2402.05980 · HTML: https://mlsanity.com/paper/2402.05980/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 45 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [OctoPack: Instruction Tuning Code Large Language Models](https://mlsanity.com/paper/2308.07124/) (2023)
- [ReCode: Robustness Evaluation of Code Generation Models](https://mlsanity.com/paper/2212.10264/) (2022)
- [CURE: Code-Aware Neural Machine Translation for Automatic Program Repair](https://mlsanity.com/paper/2103.00073/) (2021)
- [Repair Is Nearly Generation: Multilingual Program Repair with LLMs](https://mlsanity.com/paper/2208.11640/) (2022)
- [Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations](https://mlsanity.com/paper/2106.12723/) (2021)
- [CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models](https://mlsanity.com/paper/2206.00052/) (2022)
- [Semantic Robustness of Models of Source Code](https://mlsanity.com/paper/2002.03043/) (2020)
- [Typilus: Neural Type Hints](https://mlsanity.com/paper/2004.10657/) (2020)
- [Counterfactual Explanations for Models of Code](https://mlsanity.com/paper/2111.05711/) (2021)
- [Exploring the Robustness of Large Language Models for Solving Programming Problems](https://mlsanity.com/paper/2306.14583/) (2023)
- [Toward a Theory of Causation for Interpreting Neural Code Models](https://mlsanity.com/paper/2302.03788/) (2023)
- [Evaluating and Explaining Large Language Models for Code Using Syntactic Structures](https://mlsanity.com/paper/2308.03873/) (2023)
- [Recovering Variable Names for Minified Code with Usage Contexts](https://mlsanity.com/paper/1906.03488/) (2019)

## Built on by (most cited first, 14 of 14)

- [Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges](https://mlsanity.com/paper/2502.08680/) (2025)
- [IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators](https://mlsanity.com/paper/2403.03894/) (2024)
- [Large Language Model Reasoning Failures](https://mlsanity.com/paper/2602.06176/) (2026)
- [Understanding How CodeLLMs (Mis)Predict Types with Activation Steering](https://mlsanity.com/paper/2404.01903/) (2024)
- [Neuron-Guided Interpretation of Code LLMs: Where, Why, and How?](https://mlsanity.com/paper/2512.19980/) (2025)
- [When Names Disappear: Revealing What LLMs Actually Understand About Code](https://mlsanity.com/paper/2510.03178/) (2025)
- [Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility](https://mlsanity.com/paper/2601.13398/) (2026)
- [Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension Ability](https://mlsanity.com/paper/2411.19456/) (2024)
- [Trust Me, I Know This Function: Hijacking LLM Static Analysis using Bias](https://mlsanity.com/paper/2508.17361/) (2025)
- [Evaluating Code Reasoning Abilities of Large Language Models Under Real-World Settings](https://mlsanity.com/paper/2512.14917/) (2025)
- [CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems](https://mlsanity.com/paper/2407.13922/) (2024)
- [Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through Counterfactuals](https://mlsanity.com/paper/2505.14597/) (2025)
- [Auto-SPT: Automating Semantic Preserving Transformations for Code](https://mlsanity.com/paper/2512.06042/) (2025)
- [(How) Do Large Language Models Understand High-Level Message Sequence Charts?](https://mlsanity.com/paper/2605.13773/) (2026)

## Abstract (arXiv)

Large Language Models' success on text generation has also made them better at code generation and coding tasks. While a lot of work has demonstrated their remarkable performance on tasks such as code completion and editing, it is still unclear as to why. We help bridge this gap by exploring to what degree auto-regressive models understand the logical constructs of the underlying programs. We propose Counterfactual Analysis for Programming Concept Predicates (CACP) as a counterfactual testing framework to evaluate whether Large Code Models understand programming concepts. With only black-box access to the model, we use CACP to evaluate ten popular Large Code Models for four different programming concepts. Our findings suggest that current models lack understanding of concepts such as data flow and control flow.