Understanding Neural Code Intelligence Through Program Simplification (2106.03353v2)

Abstract: A wide range of code intelligence (CI) tools, powered by deep neural networks, have been developed recently to improve programming productivity and perform program analysis. To reliably use such tools, developers often need to reason about the behavior of the underlying models and the factors that affect them. This is especially challenging for tools backed by deep neural networks. Various methods have tried to reduce this opacity in the vein of "transparent/interpretable-AI". However, these approaches are often specific to a particular set of network architectures, even requiring access to the network's parameters. This makes them difficult to use for the average programmer, which hinders the reliable adoption of neural CI systems. In this paper, we propose a simple, model-agnostic approach to identify critical input features for models in CI systems, by drawing on software debugging research, specifically delta debugging. Our approach, SIVAND, uses simplification techniques that reduce the size of input programs of a CI model while preserving the predictions of the model. We show that this approach yields remarkably small outputs and is broadly applicable across many model architectures and problem domains. We find that the models in our experiments often rely heavily on just a few syntactic features in input programs. We believe that SIVAND's extracted features may help understand neural CI systems' predictions and learned behavior.

Understanding Neural Code Intelligence through Program Simplification: An Examination of the Sivand Approach

In the domain of code intelligence (CI), understanding and interpreting neural models is a pivotal concern for both researchers and practitioners. The paper "Understanding Neural Code Intelligence through Program Simplification" by Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, and Mohammad Amin Alipour, explores a novel approach to aid model interpretability by reducing the complexity of code inputs while preserving the predictions made by CI models. This technique leverages delta debugging, traditionally used in software testing, to simplify code input to neural models without loss of predictive accuracy.

Core Approach and Findings

The primary contribution of this work is the introduction of a model-agnostic methodology, Sivand, designed to reduce the code inputs fed into CI models. Through iterative simplification based on delta debugging, Sivand distills input programs into their most essential features, thereby revealing the critical elements that influence model predictions. This approach does not rely on access to model architectures or parameters, thus allowing a broader application across various CI systems.

The results demonstrated within the paper across tasks like method name prediction and variable misuse detection—utilizing models such as Code2Vec, Code2Seq, RNN, and Transformer—reveal a striking insensitivity to the overall structure and content of input programs. Instead, these models primarily depend on small syntactic patterns or tokens within the code. Notably, Sivand was able to reduce input sizes by an average of more than 60% while retaining prediction accuracy, highlighting the models' reliance on minimal input features.

Implications on Model Transparency and Robustness

The findings challenge assumptions about the holistic processing of code by CI models, revealing that these models might favor simplistic, localized explanations over comprehensive interpretations. The reduced input programs suggest that models have developed shortcuts or biases, largely depending on isolated program components rather than leveraging broader structural understanding. This has implications for the design and deployment of CI systems, particularly in safety-critical applications where understanding model reasoning is crucial.

Moreover, the simplification approach presented here could enhance model robustness by exposing potential vulnerabilities to adversarial attacks wherein slight changes to key tokens could significantly alter predictions. Considering these aspects, Sivand provides a practical tool for diagnostic and development purposes, enabling insights into model behavior without intricate knowledge of their internal workings.

Future Directions

Looking forward, the paper suggests that the implementation of multi-task learning could mitigate the observed reliance on shortcuts by diversifying model objectives, potentially encouraging more comprehensive processing of code. Additionally, integrating Sivand into developer-oriented tools may democratize access to model insights, empowering practitioners to better understand and trust the CI systems they employ.

The authors also propose further exploration into extending this interpretive methodology to models and tasks with more complex feature dependencies. This could involve integrating hierarchical reductions based on syntactic structures or exploring alternative semantic-preserving token transformations to achieve richer insights into model behavior.

In summary, the paper paves the way for enhancing the transparency and utility of neural CI models, offering a systematic approach to explore the facets of model predictions via input simplification. This method could play a fundamental role in evolving model interpretability frameworks, ensuring neural CI systems are not only accurate but also intelligible to those who use them.