Google Summer of Code: Student Motivations and Contributions
October 13, 2019 Β· Declared Dead Β· π Journal of Systems and Software
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jefferson O. Silva, Igor Wiese, Daniel M. German, Christoph Treude, Marco A. Gerosa, Igor Steinmacher
arXiv ID
1910.05798
Category
cs.SE: Software Engineering
Citations
34
Venue
Journal of Systems and Software
Last Checked
4 months ago
Abstract
Several open source software (OSS) projects expect to foster newcomers' onboarding and to receive contributions by participating in engagement programs, like Summers of Code. However, there is little empirical evidence showing why students join such programs. In this paper, we study the well-established Google Summer of Code (GSoC), which is a 3-month OSS engagement program that offers stipends and mentors to students willing to contribute to OSS projects. We combined a survey (students and mentors) and interviews (students) to understand what motivates students to enter GSoC. Our results show that students enter GSoC for an enriching experience, not necessarily to become frequent contributors. Our data suggest that, while the stipends are an important motivator, the students participate for work experience and the ability to attach the name of the supporting organization to their resumΓ©s. We also discuss practical implications for students, mentors, OSS projects, and Summer of Code programs.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted