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

This work proposes a dynamic model of developer learning in open source software (OSS) projects. A Hidden Markov Model (HMM) is proposed to explain how the code contribution behaviors of OSS developers change as their levels of knowledge on their projects increase. In this model, discrete hidden states represent the unobserved knowledge levels of developers, and their observed code contribution behaviors are modeled as state dependent. Developers’ knowledge levels evolve as they learn about the projects over time. Two modes of learning are considered: learning-by-doing (code development) and learning through interactions with peers. The model is calibrated using data spanning six years for 25 OSS projects and 251 developers hosted at Sourceforge. The proposed model identifies three knowledge states (high, medium, and low) and estimates the impact of the two modes of learning on the transition of developers between the three knowledge states. The model results suggest that in the low knowledge state developers exhibit the greatest inertia, followed by those in the medium and high states. Both modes of learning are found to have varying impact across the three knowledge states. Interactions with peers appear to be an important source of learning for developers in all states. A developer in the low state learns only through participation in threads started by others. Prior code contribution and starting discussion by initiating threads do not impact the knowledge level of a developer in the low state. Initiating threads, participating in threads started by others, and prior code contributions have positive impacts on the knowledge level of a developer in the medium or high state and, hence, influence his long term code contribution behavior. Explanations for these varying impacts of learning activities on the transitions of developers between the three states are provided. We also find a lack of persistence of knowledge in all states. The HMM better describes the data than a latent class model which would suggests that the learning activities have a long term, dynamic impact, rather than an immediate, static impact on the code contribution behavior of a developer.

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