A PERFORMANCE ANALYSIS ON MACHINE LEARNING ALGORITHMS TO PREDICT MOBILE APPLICATION’S STAR RATING BY ITS USER INTERFACE FEATURES

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)

Maryam Navaei (Creator)

Institution

East Carolina University (ECU )

Web Site: http://www.ecu.edu/lib/

Abstract: The first part of this thesis concludes with an overall summary of the publications so far on the applied Machine Learning techniques in different phases of the Software Development Life Cycle that including Requirements Analysis, Design, Implementation, Testing, and Maintenance. We have performed a systematic review of the research studies published from 2015-2021 and revealed that the Software Requirements Analysis phase has the least number of papers published\; in contrast, Software Testing is the phase with the greatest number of papers published. \n The second part of this thesis compares multiple Machine Learning algorithms for predicting mobile application star ratings by its user interface features. User interface features offer a great source of information that can be utilized by various Machine Learning algorithms to generate this prediction. To do so, we have developed and selected multiple user interface features extracted from the largest mobile user interface design prediction dataset that is available to the public, RICO repository. We initially employed the Machine Learning algorithms to a subset from RICO and then compared our results against the actual dataset using the same algorithms. Furthermore, we calculated Accuracy, Recall, and Precision for each algorithm before and after cross-validation, and showcased our results in various charts. The ultimate results demonstrate that our methodology works to predict the star rating of Android mobile applications utilizing the features we extracted from RICO dataset.

Additional Information

Publication

Thesis

Language: English

Date: 2023

Subjects

Machine Learning, Software Engineering, User Interface, Design Prediction, Mobile Application, Star Rating

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