Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations

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Abstract

Metamorphic testing is a well-established testing technique that has been successfully applied in various domains, including testing deep learning models to assess their robustness against data noise or malicious input. Currently, metamorphic testing approaches for machine learning (ML) models focused on image processing and object recognition tasks. Hence, these approaches cannot be ap- plied to ML targeting program analysis tasks. In this paper, we extend metamorphic testing approaches for ML models targeting software programs. We present Lampion, a novel testing frame- work that applies (semantics preserving) metamorphic transforma- tions on the test datasets. Lampion produces new code snippets equivalent to the original test set but different in their identifiers or syntactic structure. We evaluate Lampion against CodeBERT, a state-of-the-art ML model for Code-To-Text tasks that creates Javadoc summaries for given Java methods. Our results show that simple transformations significantly impact the target model be- havior, providing additional information on the models reasoning apart from the classic performance metric.
Original languageEnglish
Title of host publicationIEEE/ACM International Conference on Automated Software Engineering - NIER Track
PublisherIEEE / ACM
Publication statusAccepted/In press - Sep 2021

Keywords

  • Metamorphic Testing
  • Machine Learning
  • Documentation Generation
  • Code-To-Text
  • Deep learning

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