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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Veröffentlicht in:Computers (Basel) Jg. 13; H. 10; S. 266
Hauptverfasser: Rashid, Tahir, Illahi, Inam, Umer, Qasim, Jaffar, Muhammad Arfan, Ramay, Waheed Yousuf, Hakami, Hanadi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2024
Schlagworte:
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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Zusammenfassung: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.
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ISSN:2073-431X
2073-431X
DOI:10.3390/computers13100266