Unsupervised Extraction of Test Scenarios from Time-Series Sensor Data using Trace Graphs
Files
Date
2024-01-03
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
7322
Ending Page
Alternative Title
Abstract
The dependability of autonomous agents, such as self-driving cars, in complex and unpredictable real-world environments is a critical challenge. To address this, scenario-based testing attempts to assess agents across a range of diverse scenarios. However, the manual definition of these test scenarios is often labor-intensive and requires considerable domain expertise, while existing methods to extract scenarios from sensor data also depend on intricate assumptions about the data. To overcome these limitations, we introduce an unsupervised approach that uses trace graphs to determine meaningful scenario boundaries in time-series sensor data, without the need for additional domain knowledge or manual input. Our experimental results demonstrate that our method can extract a small but comprehensive set of test scenarios that captures the full spectrum of the agent's experiences as observed in the sensor data.
Description
Keywords
AI-based Methods and Applications for Software Engineering, scenario-based testing, test case mining, trace graphs, unsupervised learning
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.