An Empirical Analysis of Compatibility Issues for Industrial Mobile Games
- URL: http://arxiv.org/abs/2504.20261v1
- Date: Mon, 28 Apr 2025 21:06:23 GMT
- Title: An Empirical Analysis of Compatibility Issues for Industrial Mobile Games
- Authors: Zihe Song, Yingfeng Chen, Lei Ma, Shangjie Lu, Honglei Lin, Changjie Fan, Wei Yang,
- Abstract summary: We conduct an empirical study on common compatibility issues in commercial mobile games.<n>We build a comprehensive dataset linking bugs and fixes, enabling investigation into prevalent symptoms, root causes, and fixing strategies.<n>Our findings provide practical guidance for developers and offer insights to inspire future research on testing and fixing compatibility issues in mobile games.
- Score: 32.46314843801033
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Detecting and fixing compatibility issues is critical for mobile game development. The rapid evolution of mobile operating systems and device fragmentation make it challenging for developers to timely address these issues across diverse models. Undetected compatibility problems can severely impact user experience and cause financial loss to companies and players. However, mobile game testing remains highly challenging, and compatibility issues are largely underexplored by the research community. To bridge this gap, we conduct an empirical study on common compatibility issues in commercial mobile games. We select four active and representative games with well-documented bug reports, totaling over seven million lines of code and over 20,000 commits. We build a comprehensive dataset linking bugs and fixes, enabling investigation into prevalent symptoms, root causes, and fixing strategies. Through extensive manual analysis, we categorize the most common symptoms and root causes, and summarize the typical fixes for each category. Our findings provide practical guidance for developers and offer insights to inspire future research on testing and fixing compatibility issues in mobile games.
Related papers
- Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks [85.48034185086169]
Mobile-Agent-E is a hierarchical multi-agent framework capable of self-evolution through past experience.
Mobile-Agent-E achieves a 22% absolute improvement over previous state-of-the-art approaches.
arXiv Detail & Related papers (2025-01-20T20:35:46Z) - Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps [6.015259590468495]
FixAlly is an automated tool designed to suggest source code fixes for accessibility issues detected by automated accessibility scanners.
Our empirical study demonstrates FixAlly's capability in suggesting fixes that resolve issues found by accessibility scanners.
arXiv Detail & Related papers (2024-08-07T15:06:07Z) - Demystifying Device-specific Compatibility Issues in Android Apps [10.12578477875438]
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.
arXiv Detail & Related papers (2024-08-03T15:34:58Z) - 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 Survey of Neural Code Intelligence: Paradigms, Advances and Beyond [84.95530356322621]
This survey presents a systematic review of the advancements in code intelligence.<n>It covers over 50 representative models and their variants, more than 20 categories of tasks, and an extensive coverage of over 680 related works.<n>Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence.
arXiv Detail & Related papers (2024-03-21T08:54:56Z) - Deriving and Evaluating a Detailed Taxonomy of Game Bugs [2.2136561577994858]
The goal of this work is to provide a bug taxonomy for games that will help game developers in developing bug-resistant games.
We performed a Multivocal Literature Review (MLR) by analyzing 436 sources, out of which 189 (78 academic and 111 grey) sources reporting bugs encountered in the game development industry were selected for analysis.
The MLR allowed us to finalize a detailed taxonomy of 63 game bug categories in end-user perspective.
arXiv Detail & Related papers (2023-11-28T09:51:42Z) - Microservice API Evolution in Practice: A Study on Strategies and
Challenges [45.085830389820956]
loose coupling poses new challenges to the API evolution process.
We conducted 17 semi-structured interviews with developers, architects, and managers in 11 companies.
We identified six strategies and six challenges for REpresentational State Transfer (REST) and event-driven communication via message brokers.
arXiv Detail & Related papers (2023-11-14T14:04:17Z) - Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
We propose using scorable negotiation to evaluate Large Language Models (LLMs)
To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities.
We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
arXiv Detail & Related papers (2023-09-29T13:33:06Z) - Tachikuma: Understading Complex Interactions with Multi-Character and
Novel Objects by Large Language Models [67.20964015591262]
We introduce a benchmark named Tachikuma, comprising a Multiple character and novel Object based interaction Estimation task and a supporting dataset.
The dataset captures log data from real-time communications during gameplay, providing diverse, grounded, and complex interactions for further explorations.
We present a simple prompting baseline and evaluate its performance, demonstrating its effectiveness in enhancing interaction understanding.
arXiv Detail & Related papers (2023-07-24T07:40:59Z) - 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) - SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video
Games Using Risk Based Testing and Machine Learning [62.997667081978825]
Testing video games is an increasingly difficult task as traditional methods fail to scale with growing software systems.
We present SUPERNOVA, a system responsible for test selection and defect prevention while also functioning as an automation hub.
The direct impact of this has been observed to be a reduction in 55% or more testing hours for an undisclosed sports game title.
arXiv Detail & Related papers (2022-03-10T00:47:46Z)
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.