模拟和探索移动软件生态系统的演变:我们还有多远?

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Journal of Software-Evolution and Process Pub Date : 2023-10-11 DOI:10.1002/smr.2627
Jianmao Xiao, Zhipeng Xu, Donghua Zhang, Shiping Chen, Chenyu Liu, Zhiyong Feng, Guodong Fan, Chuying Ouyang
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引用次数: 0

摘要

移动软件生态系统的健康与软件开发者、最终用户和利益相关者的利益密切相关。因此,保持移动软件生态系统的健康和正常运行至关重要。研究人员对 Android 和 iOS 等移动软件生态系统做了大量研究。然而,移动软件生态系统中隐含的演化规律尚未引起广泛关注。本文提出了一个基于社区挖掘的研究框架,用于研究移动软件生态系统的演化过程和影响因素。首先,我们基于社区检测算法从众多移动软件项目中挖掘出不断演化的生态系统。然后,我们通过识别不同时期的演化事件来分析生态系统的演化过程。此外,我们还利用多项式物流回归模型分析相关指标,总结影响演化的关键因素。同时,通过训练长短期记忆(LSTM)模型来预测演变事件,我们的预测准确率可达 75%。这项工作可用于维护和改善移动软件生态系统的健康运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Modeling and exploring the evolution of the mobile software ecosystem: How far are we?

The health of mobile software ecosystems is closely related to the interests of software developers, end-users, and stakeholders. Therefore, it is crucial to maintain the mobile software ecosystem healthy and functioning. Researchers have done considerable research on mobile software ecosystems like Android and iOS. However, the evolution laws implicit in mobile software ecosystems have not attracted widespread attention. This paper proposes a research framework for investigating the evolution process and influencing factors of mobile software ecosystems based on community mining. Firstly, we mine the evolving ecosystem from many mobile software projects based on a community detection algorithm. Then we analyze the evolution process of the ecosystem by identifying evolution events in different periods. Furthermore, we utilize the multinomial logistics regression model to analyze the relevant indicators and summarize the crucial factors affecting the evolution. Meanwhile, by training the long short term memory (LSTM) model to predict evolution events, our prediction accuracy can reach 75%. This work can be used to maintain and improve the healthy operations of mobile software ecosystems.

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来源期刊
Journal of Software-Evolution and Process
Journal of Software-Evolution and Process COMPUTER SCIENCE, SOFTWARE ENGINEERING-
自引率
10.00%
发文量
109
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Issue Information Issue Information A hybrid‐ensemble model for software defect prediction for balanced and imbalanced datasets using AI‐based techniques with feature preservation: SMERKP‐XGB Issue Information LLMs for science: Usage for code generation and data analysis
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