A Methodology for Analyzing Uptake of Software Technologies Among Developers
A Methodology for Analyzing Uptake of Software Technologies Among Developers
复制标题
分析开发人员对软件技术的采用情况的方法
DOI：
10.1109/tse.2020.2993758
复制
发表时间：
2020
影响因子：
7.4
通讯作者：
Bradley, R
中科院分区：
文献类型：
--
作者：
Mockus, A;Zaretzki, R;Bichescu, B;Bradley, R
Motivation: The question of what combination of attributes drives the adoption of a particular software technology is critical to developers. It determines both those technologies that receive wide support from the community and those which may be abandoned, thus rendering developers’ investments worthless. Aim and Context: We model software technology adoption by developers and provide insights on specific technology attributes that are associated with better visibility among alternative technologies. Thus, our findings have practical value for developers seeking to increase the adoption rate of their products. Approach: We leverage social contagion theory and statistical modeling to identify, define, and test empirically measures that are likely to affect software adoption. More specifically, we leverage a large collection of open source version control repositories (containing over 4 billion unique versions) to construct a software dependency chain for a specific set of R language source-code files. We formulate logistic regression models, where developers’ software library choices are modeled, to investigate the combination of technological attributes that drive adoption among competing data frame (a core concept for a data science languages) implementations in the R language:tidyanddata.table. To describe each technology, we quantify key project attributes that might affect adoption (e.g., response times to raised issues, overall deployments, number of open defects, knowledge base) and also characteristics of developers making the selection (performance needs, scale, and their social network). Results: We find that a quick response to raised issues, a larger number of overall deployments, and a larger number of high-score StackExchange questions are associated with higher adoption. Decision makers tend to adopt the technology that is closer to them in the technical dependency network and in author collaborations networks while meeting their performance needs. To gauge the generalizability of the proposed methodology, we investigate the spread of two popular web JavaScript frameworksAngularandReact, and discuss the results. Future work: We hope that our methodology encompassing social contagion that captures both rational and irrational preferences and the elucidation of key measures from large collections of version control data provides a general path toward increasing visibility, driving better informed decisions, and producing more sustainable and widely adopted software.
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DOI：
--
发表时间：
1999
期刊：
International Conference on Software Engineering
影响因子：
--
作者：
B. Farbey;A. Finkelstein
通讯作者：
A. Finkelstein
影响因子：
7.7
作者：
E. Anderson
通讯作者：
E. Anderson
DOI：
10.1145/2568225.2568315
发表时间：
2014-05
期刊：
Proceedings of the 36th International Conference on Software Engineering
影响因子：
--
作者：
Jason Tsay;Laura A. Dabbish;J. Herbsleb
通讯作者：
Jason Tsay;Laura A. Dabbish;J. Herbsleb
大数据系统的软件工程
影响因子：
3.3
作者：
I. Gorton;A. Bener;A. Mockus
通讯作者：
A. Mockus
软件流程设计
DOI：
--
发表时间：
1995
期刊：
影响因子：
--
作者：
J. Holdsworth
通讯作者：
J. Holdsworth