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Title: The epidemics of programming language adoption
Authors: BARREIROS, Emanoel Francisco Spósito
Keywords: Software Engineering. Technology Transfer. Diffusion of Innovations. Programming Languages Adoption. Epidemic Models. Computational Epidemiology; Engenharia de Software. Transferência de Tecnologia. Difusão de Inovações. Adoção de Linguagens de Programação. Modelos Epidemiológicos. Epidemiologia Computacional
Issue Date: 29-Aug-2016
Publisher: Universidade Federal de Pernambuco
Abstract: Context: In Software Engineering, technology transfer has been treated as a problem that concernsonly two agents (innovation and adoption agents) working together to fill the knowledge gap between them. In this scenario, the transfer is carried out in a “peer-to-peer” fashion, not changing the reality of individuals and organizations around them. This approach works well when one is just seeking the adoption of a technology by a“specific client”. However, it can not solve a common problem that is the adoption of new technologies by a large mass of potential new users. In a wider context like this, it no longer makes sense to focus on “peer-to-peer” transfer. A new way of looking at the problem is necessary. It makes more sense to approach it as diffusion of innovations, where there is an information spreading in a community, similar to that observed in epidemics. Objective: This thesis proposes a paradigm shift to show the adoption of programming languages can be formally addressed as an epidemic. This focus shift allows the dynamics of programming language adoption to be mathematically modelled as such, and besides finding models that explain the community’s behaviour when adopting programming languages, it allows some predictions to be made, helping both individuals who wish to adopt a new language that might seem to be a new industry standard, and language designers to understand in real time the adoption of a particular language by a community. Method: After a proof of concept with data from Sourceforge (2000 to 2009), data from GitHub (2009 to January 2016), a well-known open source software repository, and Stack Overflow (2008 to March 2016), a popular Q&A system for software developers, were obtained and preprocessed. Using cumulative biological growth functions, often used in epidemiological contexts, we obtained adjusted models to the data. Once with the adjusted models, we evaluated their predictive capabilities through repeated applications of hypothesis testing and statistical calculations in different versions of the models obtained after adjusting the functions to samples of different time frames from the repositories. Results: We show that programming language adoption can be formally considered an epidemiological phenomenon by adjusting a well-known mathematical function used to describe such phenomena. We also show that, using the models found, it is possible to forecast programming languages adoption. We also show that it is possible to have similar insights by observing user data, as well as data from the community itself, not using software developers as susceptible individuals. Limitations: The forecast of the adoption outcome (asymptote) needs to be taken with care because it varies depending on the sample size, which also influences the quality of forecasts in general. Unfortunately, we not always have control over the sample size, because it depends on the population under analysis. The forecast of programming language adoption is only valid for the analysed population; generalizations should be made with caution. Conclusion: Addressing programming languages adoption as an epidemiological phenomenon allows us to perform analyses not possible otherwise. We can have an overview of a population in real time regarding the use of a programming language, which allows us, as innovation agents, to adjust our technology if it is not achieving the desired “penetration”; as adoption agents, we may decide, ahead of our competitors, to adopt a seemingly promising technology that may ultimately become a standard.
Appears in Collections:Teses de Doutorado - Ciência da Computação

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