GAP: Forecasting commit activity in git projects

[en] Abandonment of active developers poses a significant risk for many open source software projects. This risk can be reduced by forecasting the future activity of contributors involved in such projects. Focusing on the commit activity of individuals involved in git repositories, this paper proposes a practicable probabilistic forecasting model based on the statistical technique of survival analysis. The model is empirically validated on a wide variety of projects accounting for 7528 git repositories and 5947 active contributors. We found that a model based on the last 20 observed days of commit activity per contributor provides the best concordance. We also found that the predictions provided by the model are generally close to actual observations, with slight underestimations for low probability predictions and slight overestimations for higher probability predictions. This model is implemented as part of an open source tool, called GAP, that predicts future commit activity.

Disciplines :

Computer science
Electrical & electronics engineering

Author, co-author :

Decan, Alexandre ; Université de Mons > Faculté des Sciences > Service des Systèmes d'information ; Université de Mons > Faculté des Sciences > Service de Génie Logiciel

Constantinou, Eleni

Mens, Tom ; Université de Mons > Faculté des Sciences > Service de Génie Logiciel

Rocha, Henrique

Language :

English

Title :

GAP: Forecasting commit activity in git projects

Publication date :

10 March 2020

Journal title :

Journal of Systems and Software

ISSN :

0164-1212

eISSN :

1873-1228

Publisher :

Elsevier, Netherlands

Volume :

165

Peer reviewed :

Peer Reviewed verified by ORBi

Research unit :

S852 - Génie Logiciel

Research institute :

R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique
R150 - Institut de Recherche sur les Systèmes Complexes

Available on ORBi UMONS :

since 21 April 2020

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