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Prediction Models and Techniques for Open Source Software Projects: A Systematic Literature Review

Prediction Models and Techniques for Open Source Software Projects: A Systematic Literature Review

M.M. Mahbubul Syeed, Imed Hammouda, Tarja Systä
Copyright: © 2014 |Volume: 5 |Issue: 2 |Pages: 39
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781466656062|DOI: 10.4018/ijossp.2014040101
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MLA

Syeed, M.M. Mahbubul, et al. "Prediction Models and Techniques for Open Source Software Projects: A Systematic Literature Review." IJOSSP vol.5, no.2 2014: pp.1-39. https://doi.org/10.4018/ijossp.2014040101

APA

Syeed, M. M., Hammouda, I., & Systä, T. (2014). Prediction Models and Techniques for Open Source Software Projects: A Systematic Literature Review. International Journal of Open Source Software and Processes (IJOSSP), 5(2), 1-39. https://doi.org/10.4018/ijossp.2014040101

Chicago

Syeed, M.M. Mahbubul, Imed Hammouda, and Tarja Systä. "Prediction Models and Techniques for Open Source Software Projects: A Systematic Literature Review," International Journal of Open Source Software and Processes (IJOSSP) 5, no.2: 1-39. https://doi.org/10.4018/ijossp.2014040101

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Abstract

Open Source Software (OSS) is currently a widely adopted approach to developing and distributing software. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This paper reports on a systematic literature survey aimed at the identification and structuring of research that offer prediction models and techniques in analyzing OSS projects. In this review, we systematically selected and reviewed 52 peer reviewed articles that were published between January, 2000 and March, 2013. The study outcome provides insight in what constitutes the main contributions of the field, identifies gaps and opportunities, and distills several important future research directions.

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