Aktivitäten pro Jahr
Abstract
Testing AI-based systems and especially when they rely on machine learning is considered a challenging task. In this paper, we contribute to this challenge considering testing neural networks utilizing mutation testing. A former paper focused on applying mutation testing to the configuration of neural networks leading to the conclusion that mutation testing can be effectively used. In this paper, we discuss a substantially extended empirical evaluation where we considered different test data and the source code of neural network implementations. In particular, we discuss whether a mutated neural network can be distinguished from the original one after learning, only considering a test evaluation. Unfortunately, this is rarely the case leading to a low mutation score. As a consequence, we see that the testing method, which works well at the configuration level of a neural network, is not sufficient to test neural network libraries requiring substantially more testing effort for assuring quality.
Originalsprache | englisch |
---|---|
Titel | Proceedings - 2020 IEEE 20th International Conference on Software Quality, Reliability, and Security, QRS 2020 |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers |
Seiten | 356-365 |
Seitenumfang | 10 |
ISBN (elektronisch) | 9781728189130 |
DOIs | |
Publikationsstatus | Veröffentlicht - 11 Dez. 2020 |
Veranstaltung | 20th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2020 - Virtual, Macau, China Dauer: 11 Dez. 2020 → 14 Dez. 2020 |
Konferenz
Konferenz | 20th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2020 |
---|---|
Land/Gebiet | China |
Ort | Virtual, Macau |
Zeitraum | 11/12/20 → 14/12/20 |
ASJC Scopus subject areas
- Software
- Artificial intelligence
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Computernetzwerke und -kommunikation
- Modellierung und Simulation
Fingerprint
Untersuchen Sie die Forschungsthemen von „Mutation Testing for Artificial Neural Networks: An Empirical Evaluation“. Zusammen bilden sie einen einzigartigen Fingerprint.Aktivitäten
-
Mutation Testing for Artificial Neural Networks: An Empirical Evaluation
Lorenz Klampfl (Redner/in)
11 Dez. 2020Aktivität: Vortrag oder Präsentation › Vortrag bei Konferenz oder Fachtagung › Science to science
-
20th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2020
Lorenz Klampfl (Teilnehmer/-in)
11 Dez. 2020 → 14 Dez. 2020Aktivität: Teilnahme an / Organisation von › Konferenz oder Fachtagung (Teilnahme an/Organisation von)