Characterization and Detection of Tail Energy Bugs in Smartphones

Abbasi, Abdul Muqtadir; Al‐tekreeti, Mustafa; Naik, Kshirasagar; Nayak, Amiya; Srivastava, Pradeep; Zaman, Musharraf

doi:10.1109/access.2018.2877395

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…However, their approach required modifying the app to perform automatic profiling. Abbasi et al [30] measured the energy consumption of different types of wake-lock types (listed in Table.1) and their effect when a user performs different actions such as pushing the home button, back button, power button, and swipe. They developed their own apps to test these events without considering the real apps that existed.…”

Section: B Dynamic Analysismentioning

confidence: 99%

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

et al. 2021

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The popularity of Android devices has increased exponentially with an increase in the number of mobile devices. Millions of online apps are used in these devices. Energy consumption of a device is a major concern for end-users, who want a long usage time on a single battery charge. The energy consumed by the app must be optimized by developers, and the available APIs must be used carefully. A wake-lock is used in apps to control the power state of the Android device and often leads to energy leakage. In this study, we detected wake-lock leaks in Android apps using machine learning. We pre-processed apps by extracting wake-lock related APIs to obtain the structural information of wake-lock usage and oversampled the data using the synthetic minority oversampling technique (SMOTE) to balance the dataset. The machine learning algorithms used to detect wake-lock leaks were first optimized using grid search to determine the best parameters. These parameters were then used in training to detect wake-lock leaks in these apps. We employed various machine learning algorithms and divided them into simple and ensemble algorithms to evaluate their efficacy. The support vector machine (SVM) and stochastic gradient boosting (SGB) were the most effective, producing 97 % and 98 % accuracy, respectively.

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Section: B Dynamic Analysismentioning

confidence: 99%

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

et al. 2021

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“…The app must be modified to insert the logging information of the different states, which must be manually inserted. Abbasi et al [60] measured the power consumption of different wake lock types and their effect when the user performs different actions on the device (home button, back button, etc.). GreenDroid [29] and E-GreenDroid [24] perform state exploration using the Java Path Finder (JPF) to detect the wake lock deactivation function.…”

Section: Dynamic Analysismentioning

confidence: 99%

Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron

Khan

Lee

et al. 2021

Electronics

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With the proliferation of mobile devices, the popularity of Android applications (apps) has increased exponentially. Efficient power consumption in a device is essential from the perspective of the user because users want their devices to work all day. Developers must properly utilize the application programming interfaces (APIs) provided by Android software development kit to optimize the power consumption of their app. Occasionally, developers fail to relinquish the resources required by their app, resulting in a resource leak. Wake lock APIs are used in apps to manage the power state of the Android smartphone, and they frequently consume more power than necessary if not used appropriately (also called energy leak). In this study, we use a multi-layer perceptron (MLP) to detect wake lock leaks in Android apps because the MLP can solve complex problems and determine similarities in graphs. To detect wake lock leaks, we extract the call graph as features from the APK and embed the instruction and neighbor information in the node’s label of the call graph. Then, the encoded data are input to an MLP model for training and testing. We demonstrate that our model can identify wake lock leaks in apps with 99% accuracy.

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A systematic review on techniques and approaches to estimate mobile software energy consumption

Schuler,

Kotsis

2024

Sustainable Computing: Informatics and Systems

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Characterization and Detection of Tail Energy Bugs in Smartphones

Cited by 7 publications

References 15 publications

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

Detecting Wake Lock Leaks in Android Apps Using Machine Learning

Wake Lock Leak Detection in Android Apps Using Multi-Layer Perceptron

A systematic review on techniques and approaches to estimate mobile software energy consumption

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