ClusterFuzz to be Open Source Now

Program's code is written in Python and Go, and distributed under the Apache 2.0 license
08 February 2019   570

Google has opened the source code for the ClusterFuzz platform, intended for fuzzing code testing using a server cluster. In addition to coordinating the execution of checks, ClusterFuzz also automates the execution of tasks such as sending a notification to developers, creating an application for a patch (issue), tracking a bug fix, and closing reports after a patch. The code is written in Python and Go, and distributed under the Apache 2.0 license. ClusterFuzz instances can run on Linux, macOS and Windows systems, as well as in various cloud environments.

Since 2011, ClusterFuzz has been used in the depths of Google to detect errors in the Chrome codebase and to ensure the operation of the OSS-Fuzz project, in the framework of which continuous fuzzing testing of open source software was organized. In total, ClusterFuzz has revealed more than 16 thousand errors in Chrome and more than 11 thousand errors in 160 open source projects participating in the OSS-Fuzz program. Due to the continuous process of checking the current code base, errors are usually caught within a few parts after the code is introduced and the changes causing them.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   656

NVIDIA has released the source code for the SPADE machine learning system (GauGAN), which allows for the synthesis of realistic landscapes based on rough sketches, as well as training models associated with the project. The system was demonstrated in March at the GTC 2019 conference, but the code was published only yesterday. The developments are open under the non-free license CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0), allowing use only for non-commercial purposes. The code is written in Python using the PyTorch framework.

Sketches are drawn up in the form of a segmented map that determines the placement of exemplary objects on the scene. The nature of the generated objects is set using color labels. For example, a blue fill turns into sky, blue into water, dark green into trees, light green into grass, light brown into stones, dark brown into mountains, gray into snow, a brown line into a road, and a blue line into the river. Additionally, based on the choice of reference images, the overall style of the composition and the time of day are determined. The proposed tool for creating virtual worlds can be useful to a wide range of specialists, from architects and urban planners to game developers and landscape designers.

Objects are synthesized by a generative-adversarial neural network (GAN), which, based on a schematic segmented map, creates realistic images by borrowing parts from a model previously trained on several million photographs. In contrast to the previously developed systems of image synthesis, the proposed method is based on the use of adaptive spatial transformation followed by transformation based on machine learning. Processing a segmented map instead of semantic markup allows you to achieve an exact match of the result and control the style.

To achieve realism, two competing neural networks are used: the generator and the discriminator (Discriminator). The generator generates images based on mixing elements of real photos, and the discriminator identifies possible deviations from real images. As a result, a feedback is formed, on the basis of which the generator begins to assemble more and more qualitative samples, until the discriminator ceases to distinguish them from the real ones.