Analyzing the Reliance of Large Language Models on Function Names and Token Overlap for Code Summarization

Core Concepts

Large language models often rely on high token overlap between code and reference descriptions, as well as informative function names, rather than understanding the underlying code structure and semantics, to perform well on code summarization tasks.

Abstract

The paper analyzes the performance of large language models (LLMs) on the task of code summarization, focusing on understanding how much these models rely on the token overlap between the code and the reference descriptions, as well as the importance of function names, compared to the actual structure and semantics of the code.
The key insights are:

LLMs tend to generate summaries by copying tokens directly from the code, taking advantage of the high token overlap between the code and the reference descriptions in standard datasets.

The performance of LLMs is strongly correlated with the amount of token overlap between the code and the reference descriptions. Models perform much better on examples where this overlap is high.

LLMs, especially smaller models like CodeT5, rely heavily on the function names in the code, rather than the underlying code structure and logic, to generate the summaries. Obfuscating or replacing the function names significantly degrades the performance.

Larger models like Llama 2 and PaLM 2 are more sensitive to the code structure and syntax compared to CodeT5, but they are still prone to being misled by incorrect function names.

The paper also investigates the use of alternative evaluation metrics like BERTScore, which are more forgiving than BLEU, and finds that they still exhibit a strong correlation with the token overlap between code and descriptions.

The findings suggest that current LLMs for code summarization may not truly understand the semantics of the code, and instead rely on superficial cues like function names and token overlap. The paper calls for the development of more comprehensive evaluation methodologies that go beyond similarity-based metrics.

Stats

The code snippets in the standard CodeXGLUE dataset have a high token overlap with their corresponding reference descriptions.
On average, 84.34% of the tokens in the reference descriptions are also present in the code.
The percentage of tokens copied from the code to the generated summaries is even higher, ranging from 78.57% to 86.95% across different models.

Quotes

"LLMs tend to generate summaries by copying tokens directly from the code, taking advantage of the high token overlap between the code and the reference descriptions in standard datasets."
"The performance of LLMs is strongly correlated with the amount of token overlap between the code and the reference descriptions. Models perform much better on examples where this overlap is high."
"LLMs, especially smaller models like CodeT5, rely heavily on the function names in the code, rather than the underlying code structure and logic, to generate the summaries."

Key Insights Distilled From

Analyzing the Performance of Large Language Models on Code Summarization

by Rajarshi Hal... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08018.pdf

Deeper Inquiries

How can we design datasets and evaluation metrics for code summarization that better capture the models' true understanding of the code semantics, rather than their ability to exploit superficial cues

To design datasets and evaluation metrics that better capture the models' true understanding of code semantics, we can implement the following strategies:

Diverse Dataset Construction: Include a wide range of code snippets with varying complexities, different programming languages, and diverse coding styles to challenge the models' comprehension abilities.
Semantic Annotation: Annotate the datasets with detailed semantic information to provide context and meaning to the code snippets, enabling the models to grasp the underlying logic and functionality.
Human Evaluation: Incorporate human judgment in the evaluation process to assess the quality and usefulness of the generated summaries beyond just token-level similarity metrics like BLEU or BERTScore.
Contextual Understanding Metrics: Develop metrics that consider the contextual understanding of the code, such as evaluating the coherence of the generated summaries with the code logic and structure.
Adversarial Testing: Introduce adversarial examples where the superficial cues are misleading or intentionally altered to test the models' robustness and true comprehension of code semantics.

What other techniques, beyond just modifying the input code, could be used to probe the inner workings of LLMs and their reasoning on code-related tasks

Beyond modifying the input code, additional techniques to probe the inner workings of LLMs and their reasoning on code-related tasks include:

Attention Visualization: Analyze the attention mechanisms of the models to understand which parts of the code they focus on during the summarization process, providing insights into their reasoning.
Error Analysis: Conduct detailed error analysis to identify common failure patterns or misinterpretations by the models, helping to uncover weaknesses in their understanding of code semantics.
Fine-grained Feature Importance: Explore feature importance techniques to determine the significance of different code elements (e.g., function names, keywords) in the model's decision-making process.
Interpretability Tools: Utilize interpretability tools like LIME or SHAP to explain the model's predictions and highlight the key factors influencing the generated summaries.
Transfer Learning Experiments: Conduct transfer learning experiments where the models are trained on one task (e.g., code summarization) and tested on related tasks (e.g., code generation), revealing insights into their generalization capabilities and underlying reasoning strategies.

How do the findings in this paper apply to other code-related tasks like code generation, code translation, or code search, where LLMs have also shown impressive performance

The findings in this paper have implications for other code-related tasks where LLMs have shown impressive performance:

Code Generation: Similar trends may be observed in code generation tasks, where models rely heavily on superficial cues like function names and token overlap between code and descriptions. Understanding these patterns can enhance the development of more robust code generation models.
Code Translation: In code translation tasks, the reliance on token overlap and surface-level similarities may impact the accuracy and quality of translated code. By addressing these issues, we can improve the fidelity and semantic coherence of translated code snippets.
Code Search: LLMs' behavior in code summarization can provide insights into their performance in code search tasks, especially in retrieving relevant code snippets based on natural language queries. Understanding how models leverage semantic cues can enhance the effectiveness of code search algorithms.