Nowadays, there is an enormous number of mobile applications that are continuously being launched to the market. As a result of this rapid process, there is a need to increase the speed of testing process using enhanced approaches. This research aims to increase the effectiveness of the graphical user interface testing process of mobile applications. This is achieved by proposing an enhanced combinatorial-based metaheuristic approach. The proposed approach aims to maximize statement and branch coverage by applying Cuckoo search, for event selection. The approach was compared to monkey, frequency, random and greedy approaches. Experiments were conducted on different mobile applications. During the same testing time duration, the proposed approach achieved higher coverage than the other approaches. The proposed approach proved its effectiveness in mobile application testing compared to the other approaches.
To protect the images and provide a more secure cipher image, DNA encoding is crucial in image encryption. Applying a single, easily detectable coding rule to the image during DNA encoding has no impact on the encryption model's security level. Therefore, using various coding rules while applying encryption to the image, dynamic DNA-coding techniques have emerged to strengthen and improve the encryption of the image and its security. This study integrates a dynamic DNA-coding method with an encryption model. The model is applied to gray-scale images, where using a predetermined coding rule, every two bits are DNA-encoded in the image. The proposed model generates the key by sending the image and its metadata to hash functions. Following that, the hyperchaotic system constructs three chaotic sequences using the key, and the Lorenz–Liu chaotic system generates a sequence of coding rules. Then, the image is passed to Arnold Transform, where the resulted image is diffused by applying five chaotic maps. Last, using the coding rules, it is DNA-encoded, provided with the chaotic sequences to DNA, and DNA-decoded. Twelve metrics were used to assess the proposed model on ten widely used images. Results show a promising improvement in performance, since it enhanced the security of the model.
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