Success prediction of android applications in a novel repository using neural networks
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ORIGINAL ARTICLE
Success prediction of android applications in a novel repository using neural networks Mehrdad Razavi Dehkordi1 · Habib Seifzadeh1,2
· Ghassan Beydoun3 · Mohammad H. Nadimi-Shahraki1,2
Received: 23 January 2018 / Accepted: 5 May 2020 © The Author(s) 2020
Abstract Nowadays, Android applications play a major role in software industry. Therefore, having a system that can help companies predict the success probability of such applications would be useful. Thus far, numerous research works have been conducted to predict the success probability of desktop applications using a variety of machine learning techniques. However, since features of desktop programs are different from those of mobile applications, they are not applicable to mobile applications. To our knowledge, there has not been a repository or even a method to predict the success probability of Android applications so far. In this research, we introduce a repository composed of 100 successful and 100 unsuccessful apps of Android operating system in Google PlayStoreTM including 34 features per application. Then, we use the repository to a neural network and other classification algorithms to predict the success probability. Finally, we compare the proposed method with the previous approaches based on the accuracy criterion. Experimental results show that the best accuracy which we achieved is 99.99%, which obtained when we used MLP and PCA, while the best accuracy achieved by the previous work in desktop platforms was 96%. However, the time complexity of the proposed approach is higher than previous methods, since the time complexities of NPR and MLP are O(n 3 ) and O(nph k oi), respectively. Keywords Repository · Success and failure · Successful application · Failed application · Data set · Android
Introduction Today, Android has a great share of smart-phone operating systems. Based on IDC statistics,1 Android holds 86.8% of the market share in the early 2016Q3. Because of the large share of this operating system, diverse applications in dif1
http://www.idc.com/promo/smartphone-market-share/os.
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Habib Seifzadeh [email protected] Mehrdad Razavi Dehkordi [email protected] Ghassan Beydoun [email protected] Mohammad H. Nadimi-Shahraki [email protected]
1
Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2
Big Bata Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3
School of Systems, Management and Leadership, University of Technology Sydney, Sydney, Australia
ferent categories are available for it, most of which have attracted users and succeeded, while some have failed to do so. Moreover, due to the development of many applications in various areas such as mobile phones, personal computers, and webpage development, their maintenance has become particularly important. Since numerous people use these applications and pages in different fields and applications and web pages with similar features are found in all areas, there is a great compe
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