Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

Competitive Crowdsourcing Software Development (CCSD) is popular among academics and industries because of its cost-effectiveness, reliability, and quality. However, CCSD is in its early stages and does not resolve major issues, including having a low solution submission rate and high project failur...

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Veröffentlicht in:Applied sciences Jg. 14; H. 2; S. 489
Hauptverfasser: Rashid, Tahir, Anwar, Shumaila, Jaffar, Muhammad Arfan, Hakami, Hanadi, Baashirah, Rania, Umer, Qasim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.01.2024
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ISSN:2076-3417, 2076-3417
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Abstract Competitive Crowdsourcing Software Development (CCSD) is popular among academics and industries because of its cost-effectiveness, reliability, and quality. However, CCSD is in its early stages and does not resolve major issues, including having a low solution submission rate and high project failure risk. Software development wastes stakeholders’ time and effort as they cannot find a suitable solution in a highly dynamic and competitive marketplace. It is, therefore, crucial to automatically predict the success of an upcoming software project before crowdsourcing it. This will save stakeholders’ and co-pilots’ time and effort. To this end, this paper proposes a well-known deep learning model called Bidirectional Encoder Representations from Transformers (BERT) for the success prediction of Crowdsourced Software Projects (CSPs). The proposed model is trained and tested using the history data of CSPs collected from TopCoder using its REST API. The outcomes of hold-out validation indicate a notable enhancement in the proposed approach compared to existing methods, with increases of 13.46%, 8.83%, and 11.13% in precision, recall, and F1 score, respectively.
AbstractList Competitive Crowdsourcing Software Development (CCSD) is popular among academics and industries because of its cost-effectiveness, reliability, and quality. However, CCSD is in its early stages and does not resolve major issues, including having a low solution submission rate and high project failure risk. Software development wastes stakeholders’ time and effort as they cannot find a suitable solution in a highly dynamic and competitive marketplace. It is, therefore, crucial to automatically predict the success of an upcoming software project before crowdsourcing it. This will save stakeholders’ and co-pilots’ time and effort. To this end, this paper proposes a well-known deep learning model called Bidirectional Encoder Representations from Transformers (BERT) for the success prediction of Crowdsourced Software Projects (CSPs). The proposed model is trained and tested using the history data of CSPs collected from TopCoder using its REST API. The outcomes of hold-out validation indicate a notable enhancement in the proposed approach compared to existing methods, with increases of 13.46%, 8.83%, and 11.13% in precision, recall, and F1 score, respectively.
Audience Academic
Author Rashid, Tahir
Baashirah, Rania
Anwar, Shumaila
Umer, Qasim
Jaffar, Muhammad Arfan
Hakami, Hanadi
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SubjectTerms BERT
classification
Competition
Competitive Crowdsourced Software Development (CCSD)
Crowdsourcing
Deep learning
Pilots and pilotage
Software
Software development
Success factors
TopCoder
Workers
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Title Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development
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Volume 14
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