Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This imbalance biases the model toward routine activities and weakens its sensitivity to true fall events. To address this challenge, we propose a personalization framework that combines semi-supervised clustering with contrastive learning to identify and balance the most informative user feedback samples. The framework is evaluated under three retraining strategies, including Training from Scratch (TFS), Transfer Learning (TL), and Few-Shot Learning (FSL), to assess adaptability across learning paradigms. Real-time experiments with ten participants show that the TFS approach achieves the highest performance, with up to a 25% improvement over the baseline, while FSL achieves the second-highest performance with a 7% improvement, demonstrating the effectiveness of selective personalization for real-world deployment.

Android apps rely on application programming interfaces (APIs) to access various functionalities of Android devices. These APIs however are regularly updated to incorporatenew features while the old APIs get deprecated. Even though the importance of updating deprecated API usages with the recommended replacement APIs has been widely recognized, it is non-trivial to update the deprecated API usages. Therefore, the usages of deprecated APIs linger in Android apps and cause compatibility issues in practice. This paper introduces GUPPY, an automated approach that utilizes large language models (LLMs) to update Android deprecated API usages. By employing carefully crafted Chain-of-Thoughts prompts, GUPPY leverages GPT-4, one of the most powerful LLMs, to update deprecated-API usages, ensuring compatibility in both the old and new API levels. Additionally, GUPPY uses GPT-4 to generate tests, identify incorrect updates, and refine the API usage through an iterative process until the tests pass or a specified limit is reached. Our evaluation, conducted on 360 benchmark API usages from 20 deprecated APIs and an additional 156 deprecated API usages from the latest API levels 33 and 34, demonstrates GUPPY’s advantages over the state-of-the-art techniques.

The combination of computer vision and artificial intelligence is fundamentally transforming a broad spectrum of industries by enabling machines to interpret and act upon visual data with high levels of accuracy. As the biggest and by far the most popular open-source computer vision library, OpenCV library provides an extensive suite of programming functions supporting real-time computer vision. Bugs in the OpenCV library can affect the downstream computer vision applications, and it is critical to ensure the reliability of the OpenCV library. This paper introduces VistaFuzz, a novel technique for harnessing large language models (LLMs) for document-guided fuzzing of the OpenCV library. Vistafuzz utilizes LLMs to parse API documentation and obtain standardized API information. Based on this standardized information, Vista Fuzz extracts constraints on individual input parameters and dependencies between these. Using these constraints and dependencies, VistaFuzz then generates new input values to systematically test each target API. We evaluate the effectiveness of Vistafuzz in testing 330 APIs in the OpenCV library, and the results show that Vistafuzz detected 17 new bugs, where 10 bugs have been confirmed, and 5 of these have been fixed.

Abstract Android dominates the mobile operating system market, yet ensuring the quality and reliability of Android applications remains a persistent challenge. The diversity of devices, screen sizes, and OS versions complicates testing, leading to fragmented adoption of best practices. Despite advancements in automated testing, there is Limited empirical evidence on how developers test Android applications and the extent to which existing tools and frameworks are utilized effectively. In this paper, we aim to investigate the current state of Android app testing, identifying key challenges, Limitations, and best practices. Specifically, we assess the adoption of automated testing, test coverage levels, and the impact of testing practices on software quality. We conduct an experimental study on 2965 open-source Android apps, examining the quantity and coverage of the tests used for open-source Android app development. We further conduct a survey to gather more insights in testing practices from Android app developers and testers. The results reveal a limited adoption of testing among Android app developers, a restricted range of testing tools and frameworks being used, and low code and API coverage in testing. This investigation shows that current Android app testing practices are lacking the use of automated testing tools and embarks on a need for more awareness and adoption of state-of-the-art testing tools and techniques.

AI-Powered Edge Computing is accelerating the integration of the cyber world with the ever-growing list of new physical IoT devices and will fundamentally change and empower the way humans interact with the world. In this paper, we prototyped and analyzed three edge computing architectures for running SmartFall, a real-time fall detection application that uses accelerometer data from the watch, to compare the trade-off in relationship to battery consumption, potential data loss, machine learning model's prediction accuracy, and latency in model inferencing. Our experiments show that running the machine learning prediction on the server using the TensorFlow native model format has achieved the best model accuracy with-out draining the battery power of the smartwatches. However, the optimal selection of the software architecture depends on the intended deployment environment, projected user numbers, users' privacy concerns, and network stability.

In an era where Android dominates the mobile operating system market, it is important to ensure high quality Android app delivery. In this paper, we delve into the essential need for effective Android app testing in a market characterized by diversity and widespread usage and empirically investigate testing practices for Android apps. We conduct an experimental study on 2965 open-source Android apps, examining the quantity and coverage of the tests used for open-source Android app development. We further conduct a survey to gather more insights in testing practices from Android app developers and testers. The results reveal a limited adoption of testing among Android app development, a restricted range of testing tools and frameworks being used, and low code and API coverage in testing. This investigation shows that current Android app testing practices are lacking the use of automated testing tools and embarks on a need for more awareness and adoption of state-of-the-art testing tools and techniques.

Forward symbolic execution is a powerful systematic software analysis technique, but suffers from the high cost of constraint solving. During symbolic execution, off-the-shelf constraint solvers are used to check the satisfiability of path conditions whenever they are updated. However, the satisfiability information is sufficient for path exploration, while the concrete solutions are needed only for special cases, e.g., when a property violation is detected. Thus, symbolic execution can be made more efficient by leveraging rapid constraint classification instead of time-consuming constraint solving when the concrete solutions are not necessary. This paper introduces ICON, a novel approach to scaling symbolic execution with intelligent constraint classification, where neural networks are utilized to classify path conditions for satisfiability. Experimental evaluation shows ICON is highly accurate in classifying path conditions, is faster than state-of-the-art techniques for conventional constraint solving, learning based constraint solving, and constraint solution reuse, and enables more efficient symbolic execution.

Android application programming interface (API) enables app developers to harness the functionalities of Android devices by interfacing with services and hardware using a Software Development Kit (SDK). However, API frequently evolves together with its associated SDK, and compatibility issues may arise when the API level supported by the underlying device differs from the API level targeted by app developers. These issues can lead to unexpected behaviors, resulting in a bad user experience. This article presents ACID, a novel approach to detecting Android API compatibility issues induced by API evolution. It detects both API invocation compatibility issues and API callback compatibility issues using API differences and static analysis of the app code. Experiments with 20 benchmark apps show that ACID is more accurate and faster than the state-of-the-art techniques in detecting API compatibility issues. The application of ACID on 2965 real-world apps further demonstrates its practical applicability. To eliminate the false positives reported by ACID, this article also presents a simple yet effective method to quickly verify the compatibility issues by selecting and executing the relevant tests from app's test suite, and experimental results demonstrate the verification method can eliminate most false positives when app's test suite has good coverage of the API usages.

Background: The continuous evolution of the Android operating system necessitates regular API updates, which may affect the functionality of Android apps. Recent studies investigated API evolution to ensure the reliability of Android apps; however, they focused on API methods alone. Aim: We aim to empirically investigate how Android API fields evolve, and how this evolution affects the compatibility of Android apps. Method: We conducted a study based on real-world app development history data involving 11098 tags out of 105 popular open-source Android apps. Results: Our study yields interesting findings, e.g., on average two API field compatibility issues exist per app, different types of checks are preferred when addressing different types of compatibility issues, and fixing compatibility issues induced by API field evolution takes more time than fixing compatibility issues induced by API method evolution. Conclusion: These findings will help developers and researchers better understand, detect, and handle Android compatibility issues induced by API field evolution.

Android has recently become one of the best platforms for mobile app development. The constant evolution of this mobile operating system results in frequent updates to its APIs, which may affect the functionality of Android apps that are built upon them. Given the high frequency of Android API updates, impact analysis plays an important role in achieving high reliability for Android apps. This paper presents Apicia, a novel approach to API change impact analysis for Android Apps. Apicia reports the impact induced when updating the target API in terms of affected program elements (i.e., classes, methods, and statements), affected tests whose executions may exhibit different behaviors due to the API update, as well as untested affected code. We evaluate Apicia on 31 real-world Android apps, and the experimental results show that it can be cost effective on regression test selection as on average only 35.31% of tests per app are affected by API update. Moreover, since many affected statements are not covered by existing tests, Apicia can assist app developers in test suite augmentation for testing these statements. These findings indicate that Apicia is a promising technique for assisting Android developers with understanding, testing, and debugging for an API update.

Android apps are developed using a Software Development Kit (SDK), where the Android application programming interface (API) enables app developers to harness the functionalities of Android devices by interacting with services and hardware. However, API frequently evolves together with its associated SDK. The mismatch between the API level supported by the device where apps are installed and the API level targeted by app developers can induce compatibility issues. These issues can manifest themselves as unexpected behaviors, including runtime crashes, creating a poor user experience. Recent studies investigated API evolution to ensure the reliability of the Android apps, however, they require improvements. This work aims to establish novel methodologies that will improve the state-of-the-art compatibility issue detection and testing approaches.

Android apps are developed using a Software Development Kit (SDK), where the Android application programming interface (API) enables app developers to harness the functionalities of Android devices by interacting with services and hardware. However, API frequently evolves together with its associated SDK. The mismatch between the API level supported by the device where apps are installed and the API level targeted by app developers can induce compatibility issues. These issues can manifest themselves as unexpected behaviors, including runtime crashes, creating a poor user experience. In this paper, we propose ACID, a novel approach to detecting compatibility issues caused by API evolution. We leverage API differences and static analysis of the source code of Android apps to detect both API invocation compatibility issues and API callback compatibility issues. Experiments on 20 benchmark apps from previous studies show that ACID is more accurate and faster in detecting compatibility issues than state-of-the-art. We also analyzed 35 more real-world apps to show the practical applicability of our approach.