Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field.

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The work is devoted to studying the feasibility of applying the convolutional neural networks with deep learning to the problems of super-resolution of satellite images. The main aim is to enhance the image details and delete the artifacts. The algorithms for resolution enhancement were studied. The training set of satellite images was prepared. The neural network was constructed and trained using the PyTorch library for the Python language and the NVIDIA Tesla K40m graphics processors. Comparison of constructed network with the classic interpolation algorithms was carried out for the reference satellite images. It was shown that the neural network gives a better quality of the images.