Improving the Robustness of Code Generation Models Through Comprehensive Perturbation-Based Training

Core Concepts

Code generation models are not robust to small perturbations, which often lead to inconsistent and incorrect generations and significantly degrade their performance. This work proposes CodeFort, a framework to improve the robustness of code generation models by generalizing a large variety of code perturbations and enabling various robust training strategies.

Abstract

The paper addresses the issue of lack of robustness in code generation models, which often fail to generate consistent and correct outputs when the input prompts are slightly perturbed. To tackle this problem, the authors introduce CodeFort, a framework that:

Classifies code perturbations into two categories - context-free and context-sensitive - based on their impact on the ground-truth completion. This distinction allows the framework to employ different robust training methods for each category.

Proposes several robust training approaches tailored to code generation models trained using Causal Language Modeling (CLM):

Data augmentation with a masking mechanism to avoid unnatural perturbed tokens
Batch augmentation, which duplicates a portion of training examples within the same batch with different perturbations
Adversarial Logits Pairing (ALP) and ALP with name-Dropout (ALPD) to improve robustness against variable and function rename transformations
Contrastive learning objectives at the sequence, token, and name levels to enhance the discrimination of representations

Extensively evaluates the proposed approaches on different sizes of CodeGen models, demonstrating significant improvements in robustness, especially against code-syntax perturbations. The authors find that their best approach, which mixes batch augmentation, ALP, and ALPD, outperforms the sub-optimal results achieved by data augmentation alone.

Surprisingly, the authors discover that the ContraSeq contrastive learning objective, which is known to be beneficial for improving the robustness of other code-related tasks, has negligible robustness improvements on CLM-based code generation models.

Stats

The paper reports the following key metrics:

Nominal Pass@1 (NP@1): Measures the nominal code generation performance on unperturbed data.
Robust Pass@10 (RP10@1): Measures the worst-case Pass@1 on 10 perturbed variants for each perturbation type and each sample.
Robust Drop%: Measures the percentage drop from RP10@1 to NP@1, indicating the relative robustness changes given perturbations.

Quotes

"Code generation models are not robust to minor perturbations in the input prompts (e.g., inserting whitespaces/typos in docstrings or substituting variable names in code), i.e., they often generate inconsistent and incorrect outputs, thus significantly degrading their impressive performance on nominal prompts and hurting user experience when deployed in real-world applications."
"Notably, the improvement in robustness against code-syntax perturbations is evidenced by a significant decrease in pass rate drop from 95.04% to 53.35%."

Key Insights Distilled From

CodeFort: Robust Training for Code Generation Models

by Yuhao Zhang,... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01567.pdf

Deeper Inquiries

How can the proposed robust training approaches be extended to other code-related tasks beyond code generation, such as code summarization or code translation?

The proposed robust training approaches, such as data augmentation, batch augmentation, adversarial logits pairing, and contrastive learning, can be extended to other code-related tasks by adapting them to suit the specific requirements of those tasks. For example:

Code Summarization: In code summarization tasks, where the goal is to generate concise summaries of code snippets, data augmentation can involve paraphrasing the summaries or adding noise to the input data to improve the model's ability to generate accurate and concise summaries. Batch augmentation can be used to expose the model to a variety of perturbed data samples, enhancing its robustness. Adversarial logits pairing can help the model learn to generate coherent and informative summaries by aligning the distributions of predicted summaries for original and perturbed inputs. Contrastive learning can be applied to improve the model's ability to capture the semantic relationships between code snippets and their summaries.

Code Translation: For code translation tasks, where the model translates code from one programming language to another, similar robust training strategies can be employed. Data augmentation can involve translating the code snippets with variations in syntax or structure to improve the model's translation accuracy. Batch augmentation can expose the model to diverse translation examples, enhancing its generalization capabilities. Adversarial logits pairing can help the model maintain consistency in translations across different perturbations. Contrastive learning can be used to improve the model's ability to capture the nuances of code translation by aligning representations of similar code snippets in different languages.

By adapting these robust training approaches to specific code-related tasks, researchers can enhance the performance and robustness of models in tasks beyond code generation.

What are the potential limitations of the current classification of code perturbations into context-free and context-sensitive categories, and how could this taxonomy be further refined or expanded?

The current classification of code perturbations into context-free and context-sensitive categories provides a structured framework for understanding the impact of perturbations on code generation models. However, there are potential limitations to this classification that could be addressed to refine or expand the taxonomy:

Limited Scope: The current classification may not cover all possible types of perturbations that can affect code generation models. There could be additional categories of perturbations, such as semantic perturbations or structural perturbations, that are not captured in the existing taxonomy.

Ambiguity: The distinction between context-free and context-sensitive perturbations may not always be clear-cut, leading to ambiguity in categorizing certain types of perturbations. Some perturbations may exhibit characteristics of both categories, making classification challenging.

Overlapping Effects: Certain perturbations may have effects that span both context-free and context-sensitive aspects, making it difficult to assign them to a single category. This overlap can complicate the classification process and limit the effectiveness of targeted robust training strategies.

To address these limitations, the taxonomy of code perturbations could be further refined or expanded by:

Introducing subcategories within context-free and context-sensitive classifications to capture more nuanced variations in perturbations.
Incorporating additional dimensions, such as severity or frequency of perturbations, to provide a more comprehensive classification framework.
Conducting empirical studies to validate the effectiveness of the current classification and identify areas for improvement or expansion based on real-world data and model performance.
By refining and expanding the taxonomy of code perturbations, researchers can enhance their understanding of the impact of perturbations on code generation models and develop more targeted and effective robust training approaches.

Given the surprising finding that the ContraSeq contrastive learning objective has negligible robustness improvements on CLM-based code generation models, what other novel contrastive learning objectives could be designed to better suit the unique characteristics of these models?

The negligible robustness improvements of the ContraSeq contrastive learning objective on CLM-based code generation models suggest the need for novel contrastive learning objectives tailored to the unique characteristics of these models. Some potential novel contrastive learning objectives that could be designed include:

Token-Level Contrastive Learning: Instead of operating at the sequence level like ContraSeq, a token-level contrastive learning objective could focus on enhancing the discrimination of representations at the token level. By comparing representations of individual tokens in original and perturbed sequences, the model can learn to generate more accurate and robust code completions.

Syntax-Aware Contrastive Learning: This objective could incorporate syntactic information into the contrastive learning process. By considering the syntactic structure of code snippets during contrastive learning, the model can better capture the syntax-aware relationships between tokens and improve its robustness against syntax perturbations.

Semantic Contrastive Learning: Semantic contrastive learning could focus on capturing the semantic relationships between code snippets and their completions. By aligning representations that capture the semantic meaning of code elements, the model can generate more contextually relevant and accurate code completions.

Adversarial Contrastive Learning: This objective could combine adversarial training principles with contrastive learning to enhance the model's robustness against adversarial perturbations. By training the model to discriminate between adversarial and non-adversarial examples, it can learn to generate more resilient and reliable code completions.

By designing novel contrastive learning objectives that address the specific challenges and requirements of CLM-based code generation models, researchers can improve the robustness and performance of these models in real-world applications.