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DeepHyperion: exploring the feature space of deep learning-based systems through illumination search

Authors:

Tahereh Zohdinasab,

Vincenzo Riccio,

Paolo TonellaAuthors Info & Claims

ISSTA 2021: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 79 - 90

https://doi.org/10.1145/3460319.3464811

Published: 11 July 2021 Publication History

Abstract

Deep Learning (DL) has been successfully applied to a wide range of application domains, including safety-critical ones. Several DL testing approaches have been recently proposed in the literature but none of them aims to assess how different interpretable features of the generated inputs affect the system's behaviour.

In this paper, we resort to Illumination Search to find the highest-performing test cases (i.e., misbehaving and closest to misbehaving), spread across the cells of a map representing the feature space of the system. We introduce a methodology that guides the users of our approach in the tasks of identifying and quantifying the dimensions of the feature space for a given domain. We developed DeepHyperion, a search-based tool for DL systems that illuminates, i.e., explores at large, the feature space, by providing developers with an interpretable feature map where automatically generated inputs are placed along with information about the exposed behaviours.

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Cited By

Giamattei LBiagiola MPietrantuono RRusso STonella P(2025)Reinforcement learning for online testing of autonomous driving systems: a replication and extension studyEmpirical Software Engineering10.1007/s10664-024-10562-530:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10664-024-10562-5
Tang SZhang ZZhou JLei LZhou YXue YFilkov VRay BZhou M(2024)LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language ModelsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695520(1497-1508)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695520
Humeniuk DBen Braiek HReid TKhomh FFilkov VRay BZhou M(2024)In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic ManipulatorsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695281(2187-2198)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695281
Show More Cited By

Index Terms

DeepHyperion: exploring the feature space of deep learning-based systems through illumination search
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

ISSTA 2021: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis

July 2021

685 pages

ISBN:9781450384599

DOI:10.1145/3460319

General Chair:
Cristian Cadar
Imperial College London, UK
,
Program Chair:
Xiangyu Zhang
Purdue University, USA

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Published: 11 July 2021

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ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis

July 11 - 17, 2021

Virtual, Denmark

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Cited By

Giamattei LBiagiola MPietrantuono RRusso STonella P(2025)Reinforcement learning for online testing of autonomous driving systems: a replication and extension studyEmpirical Software Engineering10.1007/s10664-024-10562-530:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10664-024-10562-5
Tang SZhang ZZhou JLei LZhou YXue YFilkov VRay BZhou M(2024)LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language ModelsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695520(1497-1508)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695520
Humeniuk DBen Braiek HReid TKhomh FFilkov VRay BZhou M(2024)In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic ManipulatorsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695281(2187-2198)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695281
Huang LSun WYan MLiu ZLei YLo D(2024)Neuron Semantic-Guided Test Generation for Deep Neural Networks FuzzingACM Transactions on Software Engineering and Methodology10.1145/368883534:1(1-38)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.1145/3688835
Humeniuk DKhomh FAntoniol G(2024)Reinforcement Learning Informed Evolutionary Search for Autonomous Systems TestingACM Transactions on Software Engineering and Methodology10.1145/368046833:8(1-45)Online publication date: 27-Jul-2024
https://dl.acm.org/doi/10.1145/3680468
Zohdinasab TRiccio VTonella P(2024)Focused Test Generation for Autonomous Driving SystemsACM Transactions on Software Engineering and Methodology10.1145/366460533:6(1-32)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3664605
Li ZDai JHuang ZYou NZhang YYang MChristakis MPradel M(2024)VioHawk: Detecting Traffic Violations of Autonomous Driving Systems through Criticality-Guided Simulation TestingProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680325(844-855)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680325
Aghababaeyan ZAbdellatif MDadkhah MBriand L(2024)DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural NetworksACM Transactions on Software Engineering and Methodology10.1145/364438833:6(1-29)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3644388
Duong HXu DNguyen TDwyer M(2024)Harnessing Neuron Stability to Improve DNN VerificationProceedings of the ACM on Software Engineering10.1145/36437651:FSE(859-881)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643765
Zohdinasab TDoreste A(2024)DeepHyperion-UAV at the SBFT Tool Competition 2024 - CPS-UAV Test Case Generation TrackProceedings of the 17th ACM/IEEE International Workshop on Search-Based and Fuzz Testing10.1145/3643659.3648551(49-50)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3643659.3648551
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