Concolic Testing for Deep Neural Networks

Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the li...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE) S. 109 - 119
Hauptverfasser: Sun, Youcheng, Wu, Min, Ruan, Wenjie, Huang, Xiaowei, Kwiatkowska, Marta, Kroening, Daniel
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 01.09.2018
Schlagworte:
Artificial neural networks
concolic testing
Measurement
neural networks
Robustness
Scholarships
Software engineering
Software tools
symbolic execution
Testing
ISSN:2643-1572
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then develop a coherent method for performing concolic testing to increase test coverage. Our experimental results show the effectiveness of the concolic testing approach in both achieving high coverage and finding adversarial examples.
ISSN:2643-1572
DOI:10.1145/3238147.3238172