An Empirical Study on Software Aging of Long-Running Object Detection Algorithms

Efficient and effective object detection is a key problem in Computer Vision. Numerous object detection algorithms have been developed, whose aim is to achieve two conflicting goals, namely accuracy and efficiency, while being executed in real-time with high robustness. Many of these algorithms must...

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Veröffentlicht in:IEEE International Conference on Software Quality, Reliability and Security (Online) S. 1091 - 1102
Hauptverfasser: Pietrantuono, Roberto, Cotroneo, Domenico, Andrade, Ermeson, Machida, Fumio
Format: Tagungsbericht
Sprache:Englisch
Japanisch
Veröffentlicht: IEEE 01.12.2022
Schlagworte:
Aging
Computer bugs
computer vision
Memory management
Object detection
software aging
Software algorithms
Statistical analysis
ISSN:2693-9177
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Zusammenfassung:Efficient and effective object detection is a key problem in Computer Vision. Numerous object detection algorithms have been developed, whose aim is to achieve two conflicting goals, namely accuracy and efficiency, while being executed in real-time with high robustness. Many of these algorithms must run for an extended period of time, i.e., in video surveillance or in self-driving cars - a working condition that make them subject to the risk of software aging.In this work, we focus on evaluating several object detection algorithms to understand if and to what extent they are affected by software aging. A measurement-based aging approach was adopted, with a series of long-running tests and subsequent data analysis. The results report significant trends of performance degradation, sometimes leading to aging-related failures, as well as memory consumption trends, which turned out to be the main issue across all the experiments.
ISSN:2693-9177
DOI:10.1109/QRS57517.2022.00112