A Hidden Markov Model of Developer Learning Dynamics in Open Source Software Projects
Authors: Singh, Param Vir; Tan, Yong; Youn, Nara
Journal: Information Systems Research (2011)
<jats:p> This study develops a stochastic model to capture developer learning dynamics in open source software projects (OSS). A hidden Markov model (HMM) is proposed that allows us to investigate (1) the extent to which individuals learn from their own experience and from interactions with peers, (2) whether an individual's ability to learn from these activities varies as she evolves/learns over time, and (3) to what extent individual learning persists over time. We calibrate the model based on six years of detailed data collected from 251 developers working on 25 OSS projects hosted at Sourceforge. Using the HMM, three latent learning states (high, medium, and low) are identified, and the marginal impact of learning activities on moving the developer between these states is estimated. Our findings reveal different patterns of learning in different learning states. Learning from peers appears to be the most important source of learning for developers across the three states. Developers in the medium learning state benefit the most through discussions that they initiate. On the other hand, developers in the low and the high states benefit the most by participating in discussions s…