Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development

Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfac...

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Published in:	Computers (Basel) Vol. 13; no. 10; p. 266
Main Authors:	Rashid, Tahir, Illahi, Inam, Umer, Qasim, Jaffar, Muhammad Arfan, Ramay, Waheed Yousuf, Hakami, Hanadi
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.10.2024
Subjects:	Accuracy Algorithms BERT classification Computational linguistics Crowdsourcing crowdsourcing software development Data mining Efficiency Industrial project management Language processing Machine learning Natural language interfaces Natural language processing Prediction models Project management Resource allocation Software Software development Software engineering Success factors Task scheduling TopCoder Zero-shot learning Pakistan
ISSN:	2073-431X, 2073-431X
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Abstract	Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfactory outcomes. Although existing research has focused on task scheduling, developer recommendations, and reward mechanisms, there has been insufficient attention to the support of platform moderators, or copilots, who are essential to project success. A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. This paper introduces an innovative machine learning approach designed to automate the prediction of project duration on CSD platforms. Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. Zero-shot learning algorithms exhibit superior performance, with an average accuracy of 92.76%, precision of 92.76%, recall of 99.33%, and an f-measure of 95.93%. The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction.
AbstractList	Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfactory outcomes. Although existing research has focused on task scheduling, developer recommendations, and reward mechanisms, there has been insufficient attention to the support of platform moderators, or copilots, who are essential to project success. A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. This paper introduces an innovative machine learning approach designed to automate the prediction of project duration on CSD platforms. Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. Zero-shot learning algorithms exhibit superior performance, with an average accuracy of 92.76%, precision of 92.76%, recall of 99.33%, and an f-measure of 95.93%. The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction.
Audience	Academic
Author	Rashid, Tahir Umer, Qasim Jaffar, Muhammad Arfan Ramay, Waheed Yousuf Illahi, Inam Hakami, Hanadi
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SubjectTerms	Accuracy Algorithms BERT classification Computational linguistics Crowdsourcing crowdsourcing software development Data mining Efficiency Industrial project management Language processing Machine learning Natural language interfaces Natural language processing Prediction models Project management Resource allocation Software Software development Software engineering Success factors Task scheduling TopCoder Zero-shot learning
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Title	Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development
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