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Using Classification Methods to Reinforce the Impact of Social Factors on Software Success

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Book cover Computational Science and Its Applications – ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9790))

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Abstract

Define the way of success for software can be an arduous task, especially when dealing with OSS projects. In this case, it is extremely difficult to have control over all stages of the development process. Many researchers have approached ways to identify aspects, whether social or technical, that have some impact on the success or failure of software. Despite the large number of results found, there is still no consensus among which types of attributes have a greater success impact. Thus, after identifying technical and socio-technical factors that influence the success of OSS using data-mining techniques in about 20.000 projects data from GitHub, this study aims to compare them in order to identify those which most influence in determining the success of an OSS project. The results show that it is possible to identify the status (active or dormant) in more than 90 % of the cases based, mainly, in social attributes of the project.

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Notes

  1. 1.

    It occurs when the data model fits too much to the statistical model causing deviations by measuring errors.

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Author information

Authors and Affiliations

  1. CISUC, Centre for Informatics and Systems, University of Coimbra, Coimbra, Portugal

    Eudisley Anjos & Mário Zenha-Rela

  2. CI, Informatics Center, Federal University of Paraíba, Santa Rita, Brazil

    Eudisley Anjos, Jansepetrus Brasileiro & Danielle Silva

Authors
  1. Eudisley Anjos
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  2. Jansepetrus Brasileiro
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  3. Danielle Silva
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  4. Mário Zenha-Rela
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Corresponding author

Correspondence to Eudisley Anjos .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

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Anjos, E., Brasileiro, J., Silva, D., Zenha-Rela, M. (2016). Using Classification Methods to Reinforce the Impact of Social Factors on Software Success. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9790. Springer, Cham. https://doi.org/10.1007/978-3-319-42092-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-42092-9_15

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-42092-9

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