Students' Engagement in Open Source Projects (original) (raw)
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Prediction of Developer Participation in Issues of Open Source Projects
2012
Developers of distributed open source projects use management and issues tracking tool to communicate. These tools provide a large volume of unstructured information that makes the triage of issues difficult, increasing developers' overhead. This problem is common to online communities based on volunteer participation. This paper shows the importance of the content of comments in an open source project to build a classifier to predict the participation for a developer in an issue. To design this prediction model, we used two machine learning algorithms called Naive Bayes and J48. We used the data of three Apache Hadoop subprojects to evaluate the use of the algorithms. By applying our approach to the most active developers of these subprojects we have achieved an accuracy ranging from 79% to 96%. The results indicate that the content of comments in issues of open source projects is a relevant factor to build a classifier of issues for developers. Content analysis; prediction model; issue tracking classifier; machine learning.
Newcomers Withdrawal in Open Source Software Projects: Analysis of Hadoop Common Project
2012
Collective production communities, like open source projects, are based on volunteers collaboration and require newcomers for their continuity. Newcomers face difficulties and obstacles when starting their contributions, resulting in a large withdrawal and consequent low retention rate. This paper presents an analysis of newcomers withdrawal, checking if the dropout is influenced by lack of answer, answers politeness and helpfulness, and the answer author. We have collected five years data from the developers mail list communication and task manager (Jira) discussions of Hadoop Common project. We observed the users' communication, identifying newcomers and classifying questions and answers content. For the study conducted, less than 20% of newcomers became long term contributors. There are evidences that the withdrawal is influenced by the respondents and by the type of response received. However, the lack of answer was not evidenced as a factor that influences newcomers withdrawal in the project.
2012
Developers of distributed open source projects use management and issues tracking tool to communicate. These tools provide a large volume of unstructured information that makes the triage of issues difficult, increasing developers' overhead. This problem is common to online communities based on volunteer participation. This paper shows the importance of the content of comments in an open source project to build a classifier to predict the participation for a developer in an issue. To design this prediction model, we used two machine learning algorithms called Naive Bayes and J48. We used the data of three Apache Hadoop subprojects to evaluate the use of the algorithms. By applying our approach to the most active developers of these subprojects we have achieved an accuracy ranging from 79% to 96%. The results indicate that the content of comments in issues of open source projects is a relevant factor to build a classifier of issues for developers. Content analysis; prediction model; issue tracking classifier; machine learning.
Anais do Simpósio Brasileiro de Sistemas Colaborativos (SBSC), 2020
Os programas Summer of Code, programas intensivos de desenvolvimento de software de curta duração, podem não apenas promover o desenvolvimento, mas também inspirar o engajamento de estudantes em projetos de software livre. Neste artigo, realizamos um survey com estudantes e mentores do Google Summer of Code (GSoC) para descobrir se os estudantes continuam a colaborar após o término do programa, bem como entender o que os atrai a participarem do GSoC. Escolhemos o GSoC porque ele provê a seus estudantes uma rara combinação de recompensas de participação incluindo desenvolvimento de habilidades técnicas, alavancagem da carreira, reconhecimento pelos pares e pagamento. Usamos estatística descritiva para analisar as respostas de estudantes e mentores. Nossos resultados sugerem que a principal motivação dos estudantes participarem do GSoC tem relação com alavancagem da carreira, utilizando as habilidades técnicas adquiridas para sinalizar seus talentos a empregadores. Entretanto, também ...
Using Free Software in Higher Education for Creative Purposes: The Case of OpenLab ESEV
2010
OpenLab ESEV is a recent project of the Polytechnic Institute of Viseu's School of Education that aims to promote, foster and support the use of Free/Libre Open Source Software (F/LOSS) and Free File Formats for creative and educational purposes. During its first year of existence, the project has been particularly active in the fields of document production, software training, and technical support. As an emerging project of use of F/LOSS for creative and artistic purposes, specifically the support to 3D animation students' projects, we intend to share what we've learned by discussing results and some of the changes we want to implement in the near future. The paper includes some core concepts and concerns underlying the project, together with an analysis of the achievements and difficulties, including prefered software packages and workflows and some ongoing students' work as real case scenarios.
2012
Collective production communities, like open source projects, are based on volunteers collaboration and require newcomers for their continuity. Newcomers face difficulties and obstacles when starting their contributions, resulting in a large withdrawal and consequent low retention rate. This paper presents an analysis of newcomers withdrawal, checking if the dropout is influenced by lack of answer, answers politeness and helpfulness, and the answer author. We have collected five years data from the developers mail list communication and task manager (Jira) discussions of Hadoop Common project. We observed the users' communication, identifying newcomers and classifying questions and answers content. For the study conducted, less than 20% of newcomers became long term contributors. There are evidences that the withdrawal is influenced by the respondents and by the type of response received. However, the lack of answer was not evidenced as a factor that influences newcomers withdrawal in the project.
From promise to engagement: students and learning
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Este artigo, de caráter bibliométrico, tem como objeto a aprendizagem escolar, descentralizando o professor como único responsável pela qualidade do ensino universitário. Objetiva investigar o comprometimento do aluno como aspecto essencial à sua aprendizagem. Inicia-se o trabalho através de uma interlocução com teóricos que abordam o assunto, tendo como principais referenciais de busca os bancos de dados da ANPED (Associação Nacional de Pós-Graduação e Pesquisa em Educação) e da CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior). Parte-se desses órgãos de pesquisa devido à relevância que mantêm no campo da investigação científica. O artigo aponta a necessidade de maiores estudos, nesse campo, através de intensa interlocução teórica, como também de práticas investigativas ligadas ao aluno. Palavras-chave: pesquisa bibliométrica; comprometimento; ensino e aprendizagem.
Canvas for Development of Academic Projects in Engineering: An Application in Software Engineering
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