XML-Based Reusable Component Repository for Embedded Software

Chang, Chih‐Hung; Lu, Chih-Wei; Chu, William C.; Yang, Chao-Tung; Hsiung, Pao-Ann; Hsueh, Nien-Lin; Koong, Chorng-Shiuh; Shao, Kuei-Kai

doi:10.1109/compsacw.2011.64

Cited by 3 publications

(1 citation statement)

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“…Numerous studies have been conducted on the development of different repositories, the most important of which are: Providing a source code repository for competitive programming between students [27]; Designing and implementing an open-source software repository known as OpenCom in Shanghai Component Library to describe, store, retrieve, collect, and develop open-source software and assist programmers in component-based programming [28]; introducing a reusable component repository in embedded systems, in which components data are stored in XML documents [29];…”

Section: Previous Researchmentioning

confidence: 99%

Success prediction of android applications in a novel repository using neural networks

Dehkordi

Seifzadeh

Beydoun

et al. 2020

Complex Intell. Syst.

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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$$( n^3$$ ( n 3 ) and O$$( nph^koi$$ ( n p h k o i ), respectively.

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Section: Previous Researchmentioning

confidence: 99%