ReviewViz: assisting developers perform empirical study on energy consumption related reviews for mobile applications
Pages 27 - 30
Abstract
Improving energy efficiency of mobile applications is a topic that has gained a lot of attention recently. It has been addressed in a number of ways such as identifying energy bugs and developing a catalog of energy patterns. Previous work shows that users discuss the battery related issues (energy inefficiency or energy consumption) of the apps in their reviews. However, there is no work that addresses the automatic extraction of the battery related issues from users' feedback.
In this paper, we report on a visualization tool that is developed to empirically study machine learning algorithms and text features to automatically identify the energy consumption specific reviews with the highest accuracy. Other than the common machine learning algorithms, we utilize deep learning models with different word embeddings to compare the results. Furthermore, to help the developers extract the main topics that are discussed in the reviews, two state of the art topic modeling algorithms are applied. The visualizations of the topics represent the keywords that are extracted for each topic along with a comparison with the results of string matching.
The developed web-browser based interactive visualization tool is a novel framework developed with the intention of giving the app developers insights about running time and accuracy of machine learning and deep learning models as well as extracted topics. The tool makes it easier for the developers to traverse through the extensive result set generated by the text classification and topic modeling algorithms. The dynamic-data structure used for the tool stores the baseline-results of the discussed approaches and are updated when applied on new datasets. The tool is open sourced to replicate the research results.1
References
[1]
Ning Chen, Jialiu Lin, Steven CH Hoi, Xiaokui Xiao, and Boshen Zhang. 2014. ARminer: mining informative reviews for developers from mobile app marketplace. In Proceedings of the 36th ICSE. 767--778.
[2]
X. Cheng, X. Yan, Member Y. Lan, and J. Guo. 2014. BTM: Topic Modeling over Short Texts. IEEE Transaction on Knowledge and Data Engineering (December 2014).
[3]
Shaiful Chowdhury, Stephanie Borle, Stephen Romansky, and Abram Hindle. 2019. Greenscaler: training software energy models with automatic test generation. Empirical Software Engineering 24, 4 (2019), 1649--1692.
[4]
Luis Cruz and Rui Abreu. 2019. Catalog of energy patterns for mobile applications. Empirical Software Engineering 24, 4 (2019), 2209--2235.
[5]
Jacek Dąbrowski, Emmanuel Letier, Anna Perini, and Angelo Susi. 2019. Finding and analyzing app reviews related to specific features: A research preview. In International Working Conference on Requirements Engineering: Foundation for Software Quality. Springer, 183--189.
[6]
Jos Dirksen. 2017. Expert Data Visualization: Advanced information visualization with D3.js (1st. ed.). Packt Publishing, Birmingham, United Kingdom.
[7]
U. Fayyad, G. G. Grinstein, and A. Wierse. 2001. Information Visualization in Data Mining and Knowledge Discovery (1st. ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
[8]
Cuiyun Gao, Jichuan Zeng, Michael R Lyu, and Irwin King. 2018. Online app review analysis for identifying emerging issues. In Proceedings of the 40th International Conference on Software Engineering. 48--58.
[9]
Ehsan Noei, Feng Zhang, and Ying Zou. 2019. Too Many User-Reviews, What Should App Developers Look at First? IEEE TSE (2019).
[10]
Wikipedia contributors. 2020. D3.js. https://en.wikipedia.org/wiki/D3.js [Online; accessed 01-January-2020].
[11]
C. Wilke, S. Richly, S. Götz, C. Piechnick, and U. Aßmann. 2013. Energy consumption and efficiency in mobile applications: A user feedback study. In 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing. IEEE, 134--141.
Recommendations
Reinforcement Learning and Energy-Aware Routing
FlexNets '21: Proceedings of the 4th FlexNets Workshop on Flexible Networks Artificial Intelligence Supported Network Flexibility and AgilityWe present an approach that uses Reinforcement Learning (RL) with the Random Neural Network (RNN) acting as an adaptive critic, to route traffic in a SDN network, so as to minimize a composite Goal function that includes both packet delay and energy ...
Building energy consumption models based on smartphone user’s usage patterns
AbstractThe increasing usage of smartphones in everyday tasks has been motivated many studies on energy consumption characterization aiming to improve smartphone devices’ effectiveness and increase user usage time. In this scenario, it is ...
Investigating the Use of Topic Modeling for Social Media Market Research: A South African Case Study
Computational Science and Its Applications – ICCSA 2023AbstractBusinesses are increasingly investigating the use of data science and machine learning techniques for market research. This paper investigates the use of topic modeling as a tool for social media market research, specifically the influence and ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
July 2020
158 pages
ISBN:9781450379595
DOI:10.1145/3387905
- General Chair:
- David Lo,
- Program Chairs:
- Leonardo Mariani,
- Ali Mesbah
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
In-Cooperation
- IEEE CS
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 October 2020
Check for updates
Author Tags
Qualifiers
- Short-paper
Conference
MOBILESoft '20
Sponsor:
MOBILESoft '20: IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems
July 13 - 15, 2020
Seoul, Republic of Korea
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 59Total Downloads
- Downloads (Last 12 months)9
- Downloads (Last 6 weeks)2
Reflects downloads up to 25 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in