Demystifying Device-specific Compatibility Issues in Android Apps

• URL: http://arxiv.org/abs/2408.01810v1
• Date: Sat, 3 Aug 2024 15:34:58 GMT
• Title: Demystifying Device-specific Compatibility Issues in Android Apps
• Authors: Junfeng Chen, Kevin Li, Yifei Chen, Lili Wei, Yepang Liu,
• Abstract summary: We conduct an empirical study of 197 DSC issues collected from 94 open-source repositories on GitHub. We introduce a new perspective for comprehending these issues by categorizing them into two principal groups, Functionality Breaks, and OEM Features. The different nature of functionality break issues and OEM feature issues lead to unique challenges in addressing them.
• Score: 10.12578477875438
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The Android ecosystem is profoundly fragmented due to the frequent updates of the Android system and the prevalent customizations by mobile device manufacturers. Previous research primarily focused on identifying and repairing evolution-induced API compatibility issues, with limited consideration of devices-specific compatibility issues (DSC issues). To fill this gap, we conduct an empirical study of 197 DSC issues collected from 94 open-source repositories on GitHub. We introduce a new perspective for comprehending these issues by categorizing them into two principal groups, Functionality Breaks, and OEM Features, based on their manifestations and root causes. The functionality break issues disrupt standard Android system behaviors, lead to crashes or unexpected behaviors on specific devices, and require developers to implement workarounds to preserve the original functionality. The OEM feature issues involve the introduction of device-specific functionalities or features beyond the basic Android system. The different nature of functionality break issues and OEM feature issues lead to unique challenges in addressing them. Common solutions for functionality break issues involve calling additional APIs, substituting problematic ones, or using specific parameters, while resolving OEM feature issues often relies on Android inter-component communication methods and reflection, with additional unconventional strategies. Such observations highlight the distinctive challenges in addressing DSC issues in Android apps and will facilitate the future development of testing and analysis tools targeting these issues. Our study demonstrates that Functionality break and OEM feature issues have different characteristics, and future research may need to investigate them separately.

Related papers

• PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification [73.64560354556498]
  Vision Transformer (ViT) tends to overfit on most distinct regions of training data, limiting its generalizability and attention to holistic object features. We present PartFormer, an innovative adaptation of ViT designed to overcome the limitations in object Re-ID tasks. Our framework significantly outperforms state-of-the-art by 2.4% mAP scores on the most challenging MSMT17 dataset.
  arXiv  Detail & Related papers  (2024-08-29T16:31:05Z)
• Discriminative Spatial-Semantic VOS Solution: 1st Place Solution for 6th LSVOS [68.47681139026666]
  Video object segmentation (VOS) is a crucial task in computer vision. Current VOS methods struggle with complex scenes and prolonged object motions. This report introduces a discriminative spatial-temporal VOS model.
  arXiv  Detail & Related papers  (2024-08-29T10:47:17Z)
• Automatically Analyzing Performance Issues in Android Apps: How Far Are We? [15.614257662319863]
  We conduct a large-scale comparative study of Android performance issues in real-world applications and literature. Our results show a substantial divergence exists in the primary performance concerns of researchers, developers, and users. It is crucial for our community to intensify efforts to bridge these gaps and achieve comprehensive detection and resolution of performance issues.
  arXiv  Detail & Related papers  (2024-07-06T14:43:40Z)
• A Large-scale Investigation of Semantically Incompatible APIs behind Compatibility Issues in Android Apps [13.24503570840706]
  We conduct a large-scale discovery of incompatible APIs in the Android Open Source Project (AOSP) We propose a unified framework to detect incompatible APIs, especially for semantic changes. Our approach detects 5,481 incompatible APIs spanning from version 4 to version 33.
  arXiv  Detail & Related papers  (2024-06-25T10:12:37Z)
• Same App, Different Behaviors: Uncovering Device-specific Behaviors in Android Apps [16.015694809763538]
  We present the first large-scale empirical study of device-specific behaviors in real-world Android apps. By examining the distribution of device-specific behaviors, our analysis revealed that apps within the Chinese third-party app market exhibit more relevant behaviors compared to their counterparts in Google Play. Beyond common behaviors like issue fixes and feature adaptations, we observed 33 aggressive apps, including popular ones with millions of downloads.
  arXiv  Detail & Related papers  (2024-06-14T07:58:22Z)
• Object Detectors in the Open Environment: Challenges, Solutions, and Outlook [95.3317059617271]
  The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors. This paper aims to conduct a comprehensive review and analysis of object detectors in open environments. We propose a framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes.
  arXiv  Detail & Related papers  (2024-03-24T19:32:39Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios [61.74042680711718]
  We introduce the CHiME-7 distant ASR (DASR) task, within the 7th CHiME challenge. This task comprises joint ASR and diarization in far-field settings with multiple, and possibly heterogeneous, recording devices. The goal is for participants to devise a single system that can generalize across different array geometries.
  arXiv  Detail & Related papers  (2023-06-23T18:49:20Z)
• Taming Android Fragmentation through Lightweight Crowdsourced Testing [9.752084629147854]
  We propose a novel, lightweight, crowdsourced testing approach, LAZYCOW, to tame Android fragmentation through crowdsourced efforts. Experimental results on thousands of test cases on real-world Android devices show that LAZYCOW is effective in automatically identifying and verifying API-induced compatibility issues.
  arXiv  Detail & Related papers  (2023-04-10T01:37:16Z)
• Feature Interactions on Steroids: On the Composition of ML Models [11.707367442890936]
  Lack of specifications is a key difference between traditional software engineering and machine learning. We discuss how it drastically impacts how we think about divide-and-conquer approaches to system design.
  arXiv  Detail & Related papers  (2021-05-13T17:46:29Z)
• Federated Learning: A Signal Processing Perspective [144.63726413692876]
  Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local datasets, without explicitly exchanging the data. This article provides a unified systematic framework for federated learning in a manner that encapsulates and highlights the main challenges that are natural to treat using signal processing tools.
  arXiv  Detail & Related papers  (2021-03-31T15:14:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.