Machine learning based success prediction for crowdsourcing software projects

Illahi, Inam; Liu, Hui; Umer, Qasim; Niu, Nan

doi:10.1016/j.jss.2021.110965

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…Illahi et al [4] addressed the inefficiency in software development by proposing a machine learning model to predict project success, stemming from the challenges of low submission rates and high failure rates in competitive markets. Their novel approach, leveraging a modified keyword ranking algorithm, achieved an average precision of 82.64%, recall of 86.16%, and F-measure of 84.36% in predicting project success using CNN classifiers.…”

Section: Success Prediction On the Ccsd Platformmentioning

confidence: 99%

“…CCSD platforms, e.g., TopCoder (https://www.topcoder.com, accessed on 15 March 2023) and Kaggle (https://www.kaggle.com, accessed on 15 March 2023) [4] rely heavily on a decentralized pool of individuals, i.e., TopCoder, with 1,735,550 members registered worldwide who participate in competitive tasks published on these platforms.…”

Section: Introductionmentioning

confidence: 99%

“…To predict CSP success automatically, some numerical representations of the text and classification models have been used [4]. Different M/DL-based strategies are successful in solving different issues in the CCSD domain.…”

Section: Introductionmentioning

confidence: 99%

“…Different M/DL-based strategies are successful in solving different issues in the CCSD domain. For example, recommending the appropriate developer for the required software development project [21,22], investigating the developer's history [23,24], figuring out other contributing success factors of the CCSD projects [14,25], developing simulation methods for failure prediction and task scheduling [26,27], success prediction in the CCSD [4], and quality assessment [28,29]. Authors [30,31] provided significant contributions to this field, specifically in the context of using deep learning for project success prediction.…”

Section: Introductionmentioning

confidence: 99%

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Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

Rashid,

Anwar,

Jaffar

et al. 2024

Applied Sciences

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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.

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Section: Success Prediction On the Ccsd Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

Rashid,

Anwar,

Jaffar

et al. 2024

Applied Sciences

Self Cite

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“…We are aware of potential threats to the construct validity of USA-BERT due to the choice of evaluation metrics. We employ widely adopted metrics in the research community, namely accuracy, precision, recall, and F-measure, as highlighted by Illahi et al (2019) [62]. However, it is important to acknowledge that relying heavily on these metrics may have limitations in terms of construct validity.…”

Section: E Threats To Validitymentioning

confidence: 99%

BERT-Based Sentiment Analysis for Low-Resourced Languages: A Case Study of Urdu Language

Ashraf,

Jana,

Umer

et al. 2023

IEEE Access

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Sentiment analysis holds significant importance in research projects by providing valuable insights into public opinions. However, the majority of sentiment analysis studies focus on the English language, leaving a gap in research for other low-resourced languages or regional languages, e.g., Persian, Pashto, and Urdu. Moreover, computational linguists face the challenge of developing lexical resources for these languages. In light of this, this paper presents a deep learningbased approach for Urdu Text Sentiment Analysis (USA-BERT), leveraging Bidirectional Encoder Representations from Transformers and introduces an Urdu Dataset for Sentiment Analysis-23 (UDSA-23). USA-BERT first preprocesses the Urdu reviews by exploiting BERT-Tokenizer. Second, it creates BERT embeddings for each Urdu review. Third, given the BERT embeddings, it fine-tunes a deep learning classifier (BERT). Finally, it employs the Pareto principle on two datasets (the state-of-the-art (UCSA-21) and UDSA-23) to assess USA-BERT. The assessment results demonstrate that USA-BERT significantly surpasses the existing methods by improving the accuracy and f-measure up to 26.09% and 25.87%, respectively.

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Cost Adjustment for Software Crowdsourcing Tasks Using Ensemble Effort Estimation and Topic Modeling

Yasmin

2024

Arab J Sci Eng

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Machine learning based success prediction for crowdsourcing software projects

Cited by 8 publications

References 9 publications

Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development

BERT-Based Sentiment Analysis for Low-Resourced Languages: A Case Study of Urdu Language

Cost Adjustment for Software Crowdsourcing Tasks Using Ensemble Effort Estimation and Topic Modeling

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