A Hidden Markov Model of Developer Learning Dynamics in Open Source Software Projects

Abstract

This study examines whether developers learn from their experience and from interactions with peers in OSS projects. A Hidden Markov Model (HMM) is proposed that allows us to investigate (1) the extent to which OSS developers actually learn from their own experience and from interactions with peers, (2) whether a developer's abilities to learn from these activities vary over time, and (3) to what extent developer learning persists over time. We calibrate the model on six years of detailed data collected from 251 developers working on 25 OSS projects hosted at Sourceforge. Using the HMM three 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 as the most important source of learning for developers across the three states. Developers in the medium learning state benefit 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 started by others. While in the low state, developers depend entirely upon their peers to learn whereas when in medium or high state they can also draw upon their own experiences. Explanations for these varying impacts of learning activities on the transitions of developers between the three learning states are provided.

Keywords: Open source software development, learning, communication, Hidden Markov Model

JEL Classification: C51, D71, C32, C33, C35, C50, C52, C53