Analytics on Health of Mobile Software Ecosystem Based on the Internal Operating Mechanism

Xiao, Jianmao; Chen, Shizhan; Chen, Shiping; Gao, Chao; Wu, Hongyue; Xue, Xiao; Feng, Zhiyong

doi:10.1109/scc49832.2020.00042

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Understanding the evolution of MSECO and guiding its sustainable and healthy development have become a new challenge. There has been some studies on MSECO 7–11 . For example, Cai 7 focused on the Android SECO, trying to build an infrastructure that could systematically and continuously mine the MSECO and then analyze it.…”

Section: Introductionmentioning

confidence: 99%

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

Xiao,

Xu,

Zhang

et al. 2023

J Software Evolu Process

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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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Section: Introductionmentioning

confidence: 99%

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

Xiao,

Xu,

Zhang

et al. 2023

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

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“…With the explosively increasing global old-age population, elderly care services have gradually become a key industry of social concern. Society's progress, especially the revolution and development of the Internet (Dana et al, 2022;Xiao et al, 2020a) and intelligent software (Xiao et al, 2018), has spawned a range of pension services, which has crucial impacts on older people's service selection. However, due to their particularity, the elderly group has some distinctive characteristics such as health problems, old knowledge structure, and unskilled operation of electronic products when surfing the Internet.…”

Section: Introductionmentioning

confidence: 99%

Recommendation of Healthcare Services Based on an Embedded User Profile Model

Xiao

Liu²,

Zeng

et al. 2022

International Journal on Semantic Web and Information Systems

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In recent years, as the demand for senior care services has further increased, it has become more difficult to obtain matching services from the vast amount of data. Therefore, this paper proposes a service recommendation framework PCE-CF based on an embedded user portrait model. The framework accurately describes the elderly users through four dimensions—population, society, consumption, and health—and constructs the user portrait model by embedding tags. The embedded vector of each older man is learned through the deep learning model, and different feature groups are meaningfully expressed in the transformation space. In addition, location context and dynamic interest model are introduced to process embedded vectors, and users' service preferences are predicted according to their dynamic behaviors. The experiment results show that the PCE-CF framework proposed in this paper can improve the recommendation algorithm's efficiency and have higher feasibility in personalized service recommendations.

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Analytics on Health of Mobile Software Ecosystem Based on the Internal Operating Mechanism

Cited by 2 publications

References 19 publications

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

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

Recommendation of Healthcare Services Based on an Embedded User Profile Model

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