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 PlayStore^TM 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\(( nph^koi\)), respectively.

如今，Android应用程序在软件行业中扮演着重要角色。因此，拥有一个可以帮助公司预测此类应用程序成功可能性的系统将很有用。迄今为止，已经进行了许多研究工作，以使用各种机器学习技术来预测桌面应用程序的成功概率。但是，由于桌面程序的功能与移动应用程序的功能不同，因此它们不适用于移动应用程序。据我们所知，到目前为止，还没有一种可以预测Android应用程序成功概率的存储库甚至方法。在这项研究中，我们在Google PlayStore ^TM中引入了一个由100个成功和100个不成功的Android操作系统应用组成的存储库包括每个应用程序34个功能。然后，我们将存储库用于神经网络和其他分类算法，以预测成功概率。最后，我们将基于精确度准则的方法与以前的方法进行比较。实验结果表明，我们获得的最佳精度为99.99％，这是我们使用MLP和PCA时获得的，而先前在台式机平台上的工作所达到的最佳精度为96％。但是，由于NPR和MLP的时间复杂度分别为O \（（n ^ 3 \））和O \（（nph ^ koi \）），因此该方法的时间复杂度高于以前的方法。