Investigating Software Developers' Challenges for Android Permissions in Stack Overflow

• URL: http://arxiv.org/abs/2311.00074v1
• Date: Tue, 31 Oct 2023 18:37:03 GMT
• Title: Investigating Software Developers' Challenges for Android Permissions in Stack Overflow
• Authors: Sahrima Jannat Oishwee, Natalia Stakhanova, Zadia Codabux
• Abstract summary: This study investigates the permission-related challenges developers face on the crowdsourcing platform Stack Overflow. We conducted qualitative and quantitative analyses on 3,327 permission-related questions and 3,271 corresponding answers. Our study indicates the need for clear, consistent documentation to guide the use of permissions and reduce developer misunderstanding.
• Score: 0.9821874476902969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Android permission system is a set of controls to regulate access to sensitive data and platform resources (e.g., camera). The fast evolving nature of Android permissions, coupled with inadequate documentation, results in numerous challenges for third-party developers. This study investigates the permission-related challenges developers face and the solutions provided to resolve them on the crowdsourcing platform Stack Overflow. We conducted qualitative and quantitative analyses on 3,327 permission-related questions and 3,271 corresponding answers. Our study found that most questions are related to non-evolving SDK permissions that remain constant across various Android versions, which emphasizes the lack of documentation. We classify developers' challenges into several categories: Documentation-Related, Problems with Dependencies, Debugging, Conceptual Understanding, and Implementation Issues. We further divided these categories into 12 subcategories, nine sub-subcategories, and nine sub-sub-subcategories. Our analysis shows that developers infrequently identify the restriction type or protection level of permissions, and when they do, their descriptions often contradict Google's official documentation. Our study indicates the need for clear, consistent documentation to guide the use of permissions and reduce developer misunderstanding leading to potential misuse of Android permission. These insights from this study can inform strategies and guidelines for permission issues. Future studies should explore the effectiveness of Stack Overflow solutions to form best practices and develop tools to address these problems.

Related papers

• A Comprehensive Analysis of Evolving Permission Usage in Android Apps: Trends, Threats, and Ecosystem Insights [9.172402449557264]
  Despite official Android platform documentation on proper permission usage, there are still many cases of permission abuse.<n>This study provides a comprehensive analysis of the Android permission landscape.<n>By distinguishing between benign and malicious applications, we uncover developers' evolving strategies.
  arXiv  Detail & Related papers  (2025-08-04T02:54:10Z)
• In-Context Watermarks for Large Language Models [71.29952527565749]
  In-Context Watermarking (ICW) embeds watermarks into generated text solely through prompt engineering.<n>We investigate four ICW strategies at different levels of granularity, each paired with a tailored detection method.<n>Our experiments validate the feasibility of ICW as a model-agnostic, practical watermarking approach.
  arXiv  Detail & Related papers  (2025-05-22T17:24:51Z)
• Exploring Challenges in Test Mocking: Developer Questions and Insights from StackOverflow [0.0]
  We have analyzed 25,302 questions related to Mocking on FLOW techniques.<n>We have used Latent Dirichlet Allocation for topic modeling.<n>We have analyzed the annual and relative probabilities of each category to understand the evolution of mocking-related discussions.
  arXiv  Detail & Related papers  (2025-05-13T07:23:49Z)
• Retrieval-Augmented Generation with Conflicting Evidence [57.66282463340297]
  Large language model (LLM) agents are increasingly employing retrieval-augmented generation (RAG) to improve the factuality of their responses. In practice, these systems often need to handle ambiguous user queries and potentially conflicting information from multiple sources. We propose RAMDocs (Retrieval with Ambiguity and Misinformation in Documents), a new dataset that simulates complex and realistic scenarios for conflicting evidence for a user query.
  arXiv  Detail & Related papers  (2025-04-17T16:46:11Z)
• Towards Detecting Prompt Knowledge Gaps for Improved LLM-guided Issue Resolution [3.768737590492549]
  We analyze 433 developer-ChatGPT conversations within GitHub issue threads to examine the impact of prompt knowledge gaps and conversation styles on issue resolution. We find that ineffective conversations contain knowledge gaps in 44.6% of prompts, compared to only 12.6% in effective ones.
  arXiv  Detail & Related papers  (2025-01-20T19:41:42Z)
• Developer Challenges on Large Language Models: A Study of Stack Overflow and OpenAI Developer Forum Posts [2.704899832646869]
  Large Language Models (LLMs) have gained widespread popularity due to their exceptional capabilities across various domains. This study investigates developers' challenges by analyzing community interactions on Stack Overflow and OpenAI Developer Forum.
  arXiv  Detail & Related papers  (2024-11-16T19:38:27Z)
• Open Domain Question Answering with Conflicting Contexts [55.739842087655774]
  We find that as much as 25% of unambiguous, open domain questions can lead to conflicting contexts when retrieved using Google Search. We ask our annotators to provide explanations for their selections of correct answers.
  arXiv  Detail & Related papers  (2024-10-16T07:24:28Z)
• Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions [14.005637416640448]
  This study presents a large-scale empirical analysis of accessibility discussions on Stack Overflow to identify the trends and challenges Android and iOS developers face. Our results show several challenges, including integrating assistive technologies like screen readers, ensuring accessible UI design, supporting text-to-speech across languages, and conducting accessibility testing. We envision our findings driving improvements in developer practices, research directions, tool support, and educational resources.
  arXiv  Detail & Related papers  (2024-09-12T11:13:24Z)
• Revolutionizing API Documentation through Summarization [0.0]
  API documentation can be lengthy and challenging to navigate, prompting developers to seek unofficial sources such as Stack Overflow. We employ BERTopic and extractive summarization to automatically generate concise and informative API summaries. These summaries encompass key insights like general usage, common developer issues, and potential solutions, sourced from the wealth of knowledge on Stack Overflow.
  arXiv  Detail & Related papers  (2024-01-21T01:18:08Z)
• Toward Rapid Bug Resolution for Android Apps [0.4759142872591625]
  This paper describes the existing limitations of bug reports and identifies potential strategies for addressing them. Our vision encompasses a future where the alleviation of these limitations and successful execution of our proposed new research directions can benefit both reporters and developers.
  arXiv  Detail & Related papers  (2023-12-23T18:29:06Z)
• A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents [47.39561727838956]
  We use language models to rewrite snippets from scientific documents to be read on their own. We propose a framework that decomposes the task into three stages: question generation, question answering, and rewriting.
  arXiv  Detail & Related papers  (2023-05-24T06:23:02Z)
• DAPR: A Benchmark on Document-Aware Passage Retrieval [57.45793782107218]
  We propose and name this task emphDocument-Aware Passage Retrieval (DAPR) While analyzing the errors of the State-of-The-Art (SoTA) passage retrievers, we find the major errors (53.5%) are due to missing document context. Our created benchmark enables future research on developing and comparing retrieval systems for the new task.
  arXiv  Detail & Related papers  (2023-05-23T10:39:57Z)
• Algorithmic Fairness Datasets: the Story so Far [68.45921483094705]
  Data-driven algorithms are studied in diverse domains to support critical decisions, directly impacting people's well-being. A growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair Machine Learning hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity)
  arXiv  Detail & Related papers  (2022-02-03T17:25:46Z)
• Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflow [1.8047694351309207]
  This paper proposes an automatic and novel approach for summarizing Android API methods discussed in Stack Overflow. Our approach takes the API method's name as an input and generates a natural language summary based on Stack Overflow discussions of that API method. We have conducted a survey that involves 16 Android developers to evaluate the quality of our automatically generated summaries and compare them with the official Android documentation.
  arXiv  Detail & Related papers  (2021-11-27T18:49:51Z)
• Brain-inspired Search Engine Assistant based on Knowledge Graph [53.89429854626489]
  DeveloperBot is a brain-inspired search engine assistant named on knowledge graph. It constructs a multi-layer query graph by splitting a complex multi-constraint query into several ordered constraints. It then models the constraint reasoning process as subgraph search process inspired by the spreading activation model of cognitive science.
  arXiv  Detail & Related papers  (2020-12-25T06:36:11Z)
• On the Social and Technical Challenges of Web Search Autosuggestion Moderation [118.47867428272878]
  Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. We discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search.
  arXiv  Detail & Related papers  (2020-07-09T19:22:00Z)

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.