Syntax-guided program reduction for understanding neural code intelligence models

Published in

(2022)

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TL;DR

A syntax-guided program reduction technique that considers the grammar of the input programs during reduction that is faster and provides smaller sets of key tokens in reduced programs is applied.

Abstract

Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. Recently, input program reduction techniques have been proposed to identify key features in the input programs to improve the transparency of CI models. However, this approach is syntax-unaware and does not consider the grammar of the programming language. In this paper, we apply a syntax-guided program reduction technique that considers the grammar of the input programs during reduction. Our experiments on multiple models across different types of input programs show that the syntax-guided program reduction technique is faster and provides smaller sets of key tokens in reduced programs. We also show that the key tokens could be used in generating adversarial examples for up to 65% of the input programs.

Authors

Mohammad Amin Alipour

7 papers

Md Rafiqul Islam Rabin

7 papers

Aftab Hussain

2 papers

References29 items

Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code

A Survey on Machine Learning Techniques for Source Code Analysis

Artifact for Article (SIVAND): Understanding Neural Code Intelligence Through Program Simplification

Memorization and Generalization in Neural Code Intelligence Models

Understanding neural code intelligence through program simplification

Syntax-guided program reduction for understanding neural code intelligence models

Published in

(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

A syntax-guided program reduction technique that considers the grammar of the input programs during reduction that is faster and provides smaller sets of key tokens in reduced programs is applied.

Abstract

Authors

Mohammad Amin Alipour

7 papers

Md Rafiqul Islam Rabin

7 papers

Aftab Hussain

2 papers

References29 items

Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code

A Survey on Machine Learning Techniques for Source Code Analysis

Artifact for Article (SIVAND): Understanding Neural Code Intelligence Through Program Simplification

Memorization and Generalization in Neural Code Intelligence Models

Understanding neural code intelligence through program simplification

Probing model signal-awareness via prediction-preserving input minimization

Towards demystifying dimensions of source code embeddings

On the generalizability of Neural Program Models with respect to semantic-preserving program transformations

Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations

Embedding Java Classes with code2vec: Improvements from Variable Obfuscation

AutoFocus: Interpreting Attention-Based Neural Networks by Code Perturbation

Assessing the Generalizability of Code2vec Token Embeddings

Adversarial examples for models of code

Testing Neural Program Analyzers

A Literature Study of Embeddings on Source Code

The adverse effects of code duplication in machine learning models of code

Deep learning type inference

code2seq: Generating Sequences from Structured Representations of Code

Perses: Syntax-Guided Program Reduction

code2vec: learning distributed representations of code

Learning to Represent Programs with Graphs

A Survey of Machine Learning for Big Code and Naturalness

A Convolutional Attention Network for Extreme Summarization of Source Code

Suggesting accurate method and class names

Learning natural coding conventions

Simplifying and Isolating Failure-Inducing Input

Artifact for Article (CI-DD-Perses): Syntax-Guided Program Reduction for Understanding Neural Code IntelligenceModels

Demystifying Code Summarization Models

MAPS ’22, June 13, 2022, San Diego, CA, USA

Field of Study

Computer Science

Journal Information

Name

Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming