Performance analysis of machine learning algorithms to predict mobile applications' star ratings via its user interface features
Author
Navaei, Maryam
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. 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.
Subject
Date
2022-05-05
Citation:
APA:
Navaei, Maryam.
(May 2022).
Performance analysis of machine learning algorithms to predict mobile applications' star ratings via its user interface features
(Master's Thesis, East Carolina University). Retrieved from the Scholarship.
(http://hdl.handle.net/10342/10689.)
MLA:
Navaei, Maryam.
Performance analysis of machine learning algorithms to predict mobile applications' star ratings via its user interface features.
Master's Thesis. East Carolina University,
May 2022. The Scholarship.
http://hdl.handle.net/10342/10689.
June 29, 2024.
Chicago:
Navaei, Maryam,
“Performance analysis of machine learning algorithms to predict mobile applications' star ratings via its user interface features”
(Master's Thesis., East Carolina University,
May 2022).
AMA:
Navaei, Maryam.
Performance analysis of machine learning algorithms to predict mobile applications' star ratings via its user interface features
[Master's Thesis]. Greenville, NC: East Carolina University;
May 2022.
Collections
Publisher
East Carolina University