DeepMutation: Mutation Testing of Deep Learning Systems

Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

198 Citations (Scopus)


Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The quality of the test dataset is of great importance to gain confidence of the trained models. Using an inadequate test dataset, DL models that have achieved high test accuracy may still lack generality and robustness. In traditional software testing, mutation testing is a well-established technique for quality evaluation of test suites, which analyzes to what extent a test suite detects the injected faults. However, due to the fundamental difference between traditional software and deep learning-based software, traditional mutation testing techniques cannot be directly applied to DL systems. In this paper, we propose a mutation testing framework specialized for DL systems to measure the quality of test data. To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i.e., training data and training programs). Then we design a set of model-level mutation operators that directly inject faults into DL models without a training process. Eventually, the quality of test data could be evaluated from the analysis on to what extent the injected faults could be detected. The usefulness of the proposed mutation testing techniques is demonstrated on two public datasets, namely MNIST and CIFAR-10, with three DL models.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE International Symposium on Software Reliability Engineering, ISSRE 2018
EditorsSudipto Ghosh, Bojan Cukic, Robin Poston, Roberto Natella, Nuno Laranjeiro
PublisherIEEE Computer Society
Number of pages12
ISBN (Electronic)9781538683217
Publication statusPublished - Nov 16 2018
Event29th IEEE International Symposium on Software Reliability Engineering, ISSRE 2018 - Memphis, United States
Duration: Oct 15 2018Oct 18 2018

Publication series

NameProceedings - International Symposium on Software Reliability Engineering, ISSRE
ISSN (Print)1071-9458


Other29th IEEE International Symposium on Software Reliability Engineering, ISSRE 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Safety, Risk, Reliability and Quality


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