Detecting Wake Lock Leaks in Android Apps Using Machine Learning

Khan, Muhammad Umair; Lee, Scott Uk-Jin; Abbas, Shanza; Abbas, Asad

doi:10.1109/access.2021.3110244

Cited by 2 publications

(2 citation statements)

References 55 publications

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“…Presently, new techniques in ML and artificial intelligence (AI) help us succeed in translational research in many fields ( 29 – 31 ). Given the success of ML techniques in other fields, coupled with the large amount of data available in healthcare, ML techniques will have a promising future in the medical field ( 32 – 34 ).…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients

Liu

et al. 2022

Front. Public Health

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Breast cancer (BC) was the most common malignant tumor in women, and breast infiltrating ductal carcinoma (IDC) accounted for about 80% of all BC cases. BC patients who had bone metastases (BM) were more likely to have poor prognosis and bad quality of life, and earlier attention to patients at a high risk of BM was important. This study aimed to develop a predictive model based on machine learning to predict risk of BM in patients with IDC. Six different machine learning algorithms, including Logistic regression (LR), Naive Bayes classifiers (NBC), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme gradient boosting (XGB), were used to build prediction models. The XGB model offered the best predictive performance among these 6 models in internal and external validation sets (AUC: 0.888, accuracy: 0.803, sensitivity: 0.801, and specificity: 0.837). Finally, an XGB model-based web predictor was developed to predict risk of BM in IDC patients, which may help physicians make personalized clinical decisions and treatment plans for IDC patients.

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Section: Discussionmentioning

confidence: 99%

Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients

Liu

et al. 2022

Front. Public Health

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“…These design smells stem from various sources, including bad design practices and disregarding green development principles (Rasool & Ali, 2020). They manifest as memory, energy, and time inefficiencies (Khan, Lee, Abbas, Abbas, & Bashir, 2021). Resource leaks, including memory, CPU, and energy consumption issues, are common code smells .…”

Section: Introductionmentioning

confidence: 99%

An Automated Performance Enhancement Approach for Mobile Applications

Tahir,

Zafar,

Bashir

et al. 2024

BBE

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In the rapidly evolving landscape of mobile applications, the demand for high-quality, performance-driven software is paramount. However, the swift pace of development often leads to the introduction of code smells—bad programming practices that compromise both code quality and application performance. These code smells, if left unaddressed, can result in increased memory consumption, energy consumption, and CPU utilization, ultimately leading to a suboptimal user experience. This paper presents an automated approach for the detection and refactoring of code smells in Android applications, with a focus on improving performance. The proposed approach involves the development of a plugin integrated with Android Studio, which employs static code analysis to identify code smells. The plugin encompasses a customizable rule-based framework that allows for the detection of various code smells unique to Android development. To validate the approach, a comprehensive experiment is conducted. The experiment assesses the effectiveness of the proposed method in detecting code smells and explores the impact of refactoring on application performance. The results showcase that the proposed plugin successfully detects code smells in various open-source Android applications. Moreover, the integration of refactoring recommendations significantly improves the performance of the applications, as demonstrated through memory, energy, and CPU consumption metrics. Comparison with existing tools reveals that the proposed approach offers superior performance in terms of both code smell detection and refactoring. Additionally, the approach bridges the gap left by some existing tools by identifying previously undetected code smells, such as "string concatenation." The presented method not only enhances code quality but also contributes to the overall performance optimization of Android applications. As mobile applications continue to play an increasingly central role in modern life, the importance of maintaining high-quality code that performs optimally cannot be understated. This work provides a valuable contribution towards achieving these goals, offering developers a powerful tool for ensuring that their applications not only meet but exceed user expectations in terms of quality and performance.

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Detecting Wake Lock Leaks in Android Apps Using Machine Learning

Cited by 2 publications

References 55 publications

Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients

Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients

An Automated Performance Enhancement Approach for Mobile Applications

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