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https://doi.org/10.2139/ssrn.4615255

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A comprehensive study on software aging across android versions and vendors
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Invalid bug reports complicate the software aging situation
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Does software reliability growth behavior follow a non-homogeneous Poisson process
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Adaptive software testing with fixed-memory feedback
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Software micro-rejuvenation for Android mobile systems
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A practice guide of software aging prediction in a web server based on machine learning
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Using machine learning for software aging detection in Android system
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Predicting Web Server Crashes: A Case Study in Comparing Prediction Algorithms
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Software Aging Analysis of the Android Mobile OS
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The Impact of Software Aging and Rejuvenation on the User Experience for Android System
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The fundamentals of software aging
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An Empirical Study on Software Aging Indicators Prediction in Android Mobile
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A New Software Rejuvenation Model for Android
inferredcites →https://doi.org/10.1109/issrew.2019.00073openalex-extracted
Analysis of Software Aging Impacts on Plant Anomaly Detection with Edge Computing
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2014 IEEE International Symposium on Software Reliability Engineering Workshops
inferredcites →https://doi.org/10.1109/issrew51248.2020.00092openalex-extracted
Chapter 3: Software Rejuvenation: Key Concepts and Granularity
inferredcites →https://doi.org/10.1109/issrew55968.2022.00061openalex-extracted
Sequential Performance Analysis of Systems that Age and Rejuvenate
inferredcites →https://doi.org/10.1109/iweca.2014.6845739openalex-extracted
SOM-based aging detection for Virtual Machine Monitor
inferredcites →https://doi.org/10.1109/jsee.2015.00047openalex-extracted
Neural network based approach for time to crash prediction to cope with software aging
inferredcites →https://doi.org/10.1109/noms.2008.4575113openalex-extracted
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inferredcites →https://doi.org/10.1109/prdc.2001.992684openalex-extracted
Optimal software rejuvenation policy with discounting
inferredcites →https://doi.org/10.1109/prdc.2017.27openalex-extracted
An Empirical Investigation of Fault Triggers in Android Operating System
inferredcites →https://doi.org/10.1109/qrs54544.2021.00085openalex-extracted
ACLM:Software Aging Prediction of Virtual Machine Monitor Based on Attention Mechanism of CNN-LSTM Model
inferredcites →https://doi.org/10.1109/qrs60937.2023.00049openalex-extracted
An Empirical Study to Identify Software Aging Indicators for Android OS
inferredcites →https://doi.org/10.1109/step.2003.4openalex-extracted
An Overview of Software Cybernetics
inferredcites →https://doi.org/10.1109/tdsc.2005.15openalex-extracted
A Comprehensive Model for Software Rejuvenation
inferredcites →https://doi.org/10.1109/tetc.2023.3267899openalex-extracted
Runtime System Support for CPS Software Rejuvenation
inferredcites →https://doi.org/10.1109/tr.2007.909764openalex-extracted
A Best Practice Guide to Resource Forecasting for Computing Systems
inferredcites →https://doi.org/10.1109/tr.2018.2881306openalex-extracted
Two-Level Rejuvenation for Android Smartphones and Its Optimization
inferredcites →https://doi.org/10.1109/tr.2021.3110096openalex-extracted
DeepSIM: Deep Semantic Information-Based Automatic Mandelbug Classification
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2021 World Conference on Computing and Communication Technologies (WCCCT)
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Performance Degradation Detection of Virtual Machines Via Passive Measurement and Machine Learning
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