Machine learning based success prediction for crowdsourcing software projects

A takeover success prediction model aims at predicting the probability that a takeover attempt will succeed by using publicly available information at the time of the announcement. We perform a thorough study using machine learning techniques to predict takeover success. Specifically, we model takeover success prediction as a binary classification problem, which has been widely studied in the machine learning community. Motivated by the recent advance in machine learning, we empirically evaluate and analyze many state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines with different kernels, decision trees, random forest, and Adaboost. The experiments validate the effectiveness of applying machine learning in takeover success prediction, and we found that the support vector machine with linear kernel and the Adaboost with stump weak classifiers perform the best for the task. The result is consistent with the general observations of these two approaches.

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Estimation Approaches of Machine Learning in Scrum Projects: A Review

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38977 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1110-1118

Author(s):

Chitrak Vimalbhai Dave

Keyword(s):

Machine Learning ◽

Regression Testing ◽

Process Models ◽

Effort Estimation ◽

Software Projects ◽

It Industry ◽

Resistance Factors ◽

User Story ◽

Inaccurate Estimation ◽

Agile Software

Abstract: It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx 43% of the projects are often delivered late and entered crises because of over budget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g. Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon forstrengthening the collaboration betweendeveloper and customer.Estimation has always been challenging in Agile as requirements are volatile. This encourages researchersto work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paperreviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation. Keywords: Machine Learning, Scrum, Scrum Projects, Effort Estimation, Agile Software Development

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A Model for Business Success Prediction using Machine Learning Algorithms

Journal of Physics Conference Series ◽

10.1088/1742-6596/1299/1/012050 ◽

2019 ◽

Vol 1299 ◽

pp. 012050

Author(s):

Ibukun Afolabi ◽

T. Cordelia Ifunaya ◽

Funmilayo G. Ojo ◽

Chinonye Moses

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Success Prediction ◽

Business Success

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Risk Group Prediction of Software Projects Using Machine Learning Algorithm

10.1109/ubmk52708.2021.9558957 ◽

2021 ◽

Author(s):

Asim Kerem Hanci

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Risk Group ◽

Machine Learning Algorithm ◽

Software Projects

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The Right Time for Crowd Communication during Campaigns for Sustainable Success of Crowdfunding: Evidence from Kickstarter Platform

Sustainability ◽

10.3390/su12187642 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7642 ◽

Cited By ~ 1

Author(s):

Michael J. Ryoba ◽

Shaojian Qu ◽

Ying Ji ◽

Deqiang Qu

Keyword(s):

Machine Learning ◽

At Risk ◽

Prediction Accuracy ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Success Prediction ◽

Combined Effects ◽

Baseline Model ◽

Predicting Success ◽

The Right

Only a small percentage of crowdfunding projects succeed in securing funds, the fact of which puts the sustainability of crowdfunding platforms at risk. Researchers have examined the influences of phased aspects of communication, drawn from updates and comments, on success of crowdfunding campaigns, but in most cases they have focused on the combined effects of the aspects. This paper investigated campaign success contribution of various combinations of phased communication aspects from updates and comments, the best of which can help creators to successfully manage campaigns by focusing on the important communication aspects. Metaheuristic and machine learning algorithms were used to search and evaluate the best combination of phased communication aspects for predicting success using Kickstarter dataset. The study found that the number of updates in phase one, the polarity of comments in phase two, readability of updates and polarity of comments in phase three, and the polarity of comments in phase five are the most important communication aspects in predicting campaign success. Moreover, the success prediction accuracy with the aspects identified after phasing is more than the baseline model without phasing. Our findings can help crowdfunding actors to focus on the important communication aspects leading to improved likelihood of success.

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Story Point-Based Effort Estimation Model with Machine Learning Techniques

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194020500035 ◽

2020 ◽

Vol 30 (01) ◽

pp. 43-66

Author(s):

Muaz Gultekin ◽

Oya Kalipsiz

Keyword(s):

Machine Learning ◽

Empirical Evaluation ◽

Machine Learning Algorithms ◽

Decision Makers ◽

Machine Learning Techniques ◽

Effort Estimation ◽

Estimation Model ◽

Software Projects ◽

Learning Techniques ◽

Better Than

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.

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