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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MDPI AG
2024-10-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/13/10/266 |
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| author | Tahir Rashid Inam Illahi Qasim Umer Muhammad Arfan Jaffar Waheed Yousuf Ramay Hanadi Hakami |
| author_facet | Tahir Rashid Inam Illahi Qasim Umer Muhammad Arfan Jaffar Waheed Yousuf Ramay Hanadi Hakami |
| author_sort | Tahir Rashid |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-113ee2b6366a430a9dbf96cba2fa18ba |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-113ee2b6366a430a9dbf96cba2fa18ba2025-08-20T02:11:12ZengMDPI AGComputers2073-431X2024-10-01131026610.3390/computers13100266Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software DevelopmentTahir Rashid0Inam Illahi1Qasim Umer2Muhammad Arfan Jaffar3Waheed Yousuf Ramay4Hanadi Hakami5Department of Computer Science, The Superior University, Lahore 54000, PakistanDepartment of Computing and Emerging Technologies, Emerson University, Multan 60000, PakistanDepartment of Computer Sciences, COMSATS University Islamabad, Vehari 61000, PakistanDepartment of Computer Science, The Superior University, Lahore 54000, PakistanDepartment of Computer Science, Cholistan University of Veterinary and Animal Sciences, Bahawalpur 63100, PakistanDepartment of Software Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi ArabiaCrowdsourcing 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.https://www.mdpi.com/2073-431X/13/10/266classificationBERTmachine learningcrowdsourcingcrowdsourcing software developmentTopCoder |
| spellingShingle | Tahir Rashid Inam Illahi Qasim Umer Muhammad Arfan Jaffar Waheed Yousuf Ramay Hanadi Hakami Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development Computers classification BERT machine learning crowdsourcing crowdsourcing software development TopCoder |
| title | Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development |
| title_full | Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development |
| title_fullStr | Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development |
| title_full_unstemmed | Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development |
| title_short | Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development |
| title_sort | zero shot learning for accurate project duration prediction in crowdsourcing software development |
| topic | classification BERT machine learning crowdsourcing crowdsourcing software development TopCoder |
| url | https://www.mdpi.com/2073-431X/13/10/266 |
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