Enhanced convolutional neural network based android software maintainability prediction

Abstract

Advancement in mobile technology, necessitates quality in development of android applications. To improve the quality of android application, evaluation of its maintenance has to be started from the initial stage of development. There are several existing models involved in android application maintenance evaluation, but most of them suffers from the class imbalance problem and its performance depends on the size of the dataset. This paper focuses on overcoming the class imbalance while using convolutional neural network for prediction the android application maintainability. In this work both android metrics and object-oriented metrics are used for evaluating the android application maintainability. The standard convolutional neural network is enhanced by pretraining it using evolutionary algorithm known as moth flame optimization (MFO). The MFO involves in optimizing the learning parameters of convolutional layer and after the training process is completed, with the acquired knowledge it handles the small size dataset very effectively during the testing phase. Thus, the simulation results of maintainability prediction in android application development using proposed enhanced CNN achieves better result while comparing with other standard classification models.