Enterprise AI Analysis
Understanding Code Semantics: A Benchmark Study of LLMs
This empirical study evaluates the ability of Large Language Models (LLMs) to understand code semantics by identifying equivalent and inequivalent programs. Our research shows that LLMs frequently struggle with this complex task, misclassifying 41% of semantically equivalent cases without context and still 29% with minimal context. Despite advancements in code generation, LLMs often lack the deeper reasoning required for robust semantic understanding.
Authors: Cosimo Laneve, Alvise Spanò, Dalila Ressi, Sabina Rossi, Michele Bugliesi
Accepted: February 20, 2026 | Published: March 27, 2026
Key Findings & Performance Metrics
Our comprehensive evaluation across seven state-of-the-art LLMs reveals significant challenges in semantic code understanding, particularly with nuanced transformations. While contextual prompting offers some improvement, fundamental limitations persist.
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Misclassification Rate (No Context)
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Misclassification Rate (With Context)
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Best Performer Failure Rate (Claude)
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Worst Performer Failure Rate (Gemini)
Deep Analysis & Enterprise Applications
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Empirical Study Methodology
To rigorously assess LLM code understanding, we developed a systematic approach involving deliberate program perturbations based on compiler optimizations. This allowed us to probe whether models could infer deep semantic equivalence beyond superficial syntactic variations.
Enterprise Process Flow
Formalize Semantics-Preserving Transformations (CP/CF)
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Curate Benchmark Dataset (11 Python functions, 8 variants)
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Systematically Perturb Code Samples
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Evaluate 7 LLMs with Zero-Shot Prompting
Dataset and Prompt Design
Our dataset comprised 11 distinct Python functions, each with a reference implementation, three semantically equivalent perturbed versions (copy propagation, constant folding, or both), and four incorrect versions (one bugged reference, three bugged perturbed). We used four zero-shot prompt types:
| Prompt Type | Description | Purpose |
|---|---|---|
| Prompt 1 (Single-Class, Contextless) | Brief question with 4 correct code versions. | Assess structural recognition without guidance. |
| Prompt 2 (Single-Class, Contextual) | Contextual preamble with 4 correct code versions. | Evaluate impact of added context on correct classification. |
| Prompt 3 (Multi-Class, Contextless) | Brief question with 8 mixed (correct/incorrect) code versions. | Test distinction between correct/incorrect without context. |
| Prompt 4 (Multi-Class, Contextual) | Contextual preamble with 8 mixed (correct/incorrect) code versions. | Assess if context aids mixed classification accuracy. |
LLM Performance Overview
Our experiments involved 3,080 outputs and over 15,400 fine-grained equivalence decisions. Overall, LLMs demonstrated an average accuracy of 59% for zero-shot contextless queries, which improved to 71% with contextual information. However, the significant error rate indicates persistent challenges in robust semantic understanding.
| Metric | No Context Prompts (Avg Accuracy) | With Context Prompts (Avg Accuracy) | Most Challenging (CP+CF) Avg Accuracy |
|---|---|---|---|
| Overall Average | 58.37% | 69.30% | 49.39% |
| Claude (Web) | 74.24% | 83.94% | 79.09% (Overall Correct) |
| DeepSeek | 73.76% | 80.00% | 76.88% (Overall Correct) |
| Gemini | 46.53% | 40.91% | 43.72% (Overall Correct) |
Notably, the combination of copy propagation (CP) and constant folding (CF) proved to be the most challenging perturbation, resulting in the lowest overall accuracy of 49.39%. Anthropic Claude and DeepSeek consistently emerged as top performers, while Gemini exhibited the lowest scores.
Challenges in LLM Code Understanding
Our observations revealed several intriguing behavioral patterns and inconsistencies in LLM reasoning during semantic equivalence tasks.
Inconsistencies and Hallucinations
LLMs frequently exhibited internal contradictions, starting with an affirmative judgment and later reversing it, or providing confusing double negatives. This suggests a superficial rather than robust internal reasoning process, undermining logical consistency.
Impact of Response Verbosity
When instructed to be concise, LLM answers were often more erroneous. This implies that verbose outputs, which reflect the model's "chain of thought," are crucial for accurate reasoning, and limiting verbosity can truncate essential reasoning steps.
Challenges with Data Types and Transformations
Copy propagation proved particularly difficult, especially when involving non-numerical data types like lists and arrays. LLMs struggled to differentiate between Python's assignment operator behavior (copy for numerical types vs. reference for mutable structures), leading to significant confusion.
Algorithm-Specific Performance
The Sieve of Eratosthenes yielded the lowest accuracy scores due to confusion around modulus operations and list manipulation in perturbed code. Conversely, algorithms like 3D point rotation and FFT achieved 100% success, while the Unification algorithm was consistently the least understood, likely due to its use of object-oriented case-based structures.
Recommendations for Robust Semantic Understanding
Our study underscores that while contextual prompting can provide practical performance gains, it does not fully address the underlying limitations in LLMs' semantic understanding. The existing 59% accuracy for contextless queries improving to 71% with context still leaves a significant gap for broad trust.
Strategies for Improvement
Robust semantic understanding requires advancements at both model-level and usage-level:
Model-Level Improvements:
- Targeted Fine-tuning: Adapting LLMs to reason more robustly about semantic equivalence through supervised datasets.
- Contrastive Learning: Shaping internal representations to bring equivalent code closer and push non-equivalent code apart.
Usage-Level Improvements:
- Advanced Prompt Engineering: Leveraging chain-of-thought, role-based, or instruction-tuned prompting for deeper reasoning.
- Retrieval-Augmented Generation (RAG): Injecting relevant external context to enhance outputs.
- Tool-Assisted Pre-processing: Integrating static code analysis or transformation pipelines to normalize low-level syntactic differences before LLM inference, allowing models to focus on semantic content.
Given the proprietary nature of commercial LLMs, collaboration between model developers and the research community is crucial for implementing these deeper, model-level enhancements.
Calculate Your Potential AI ROI
Estimate the impact of integrating advanced AI code understanding capabilities into your enterprise workflows. See how improved semantic analysis can save your team significant time and resources.
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Your AI Implementation Roadmap
Partner with us to navigate the complexities of integrating advanced AI for code understanding. Our phased approach ensures a seamless and impactful deployment tailored to your enterprise needs.
Phase 1: Discovery & Assessment
In-depth analysis of your current codebases, development workflows, and semantic understanding challenges. Identify key areas for AI augmentation.
Phase 2: Custom Model Engineering
Develop or fine-tune LLM models for your specific language, domain, and internal code standards, leveraging techniques like contrastive learning.
Phase 3: Integration & Tooling
Implement pre-processing pipelines and integrate AI models into your existing IDEs, CI/CD, and code review systems for optimal workflow. This includes robust transformation-invariant code handling.
Phase 4: Validation & Optimization
Thorough testing and validation against real-world scenarios. Continuous optimization and retraining to ensure high accuracy and adaptability to evolving code patterns.
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