Facebook to Unveil Getafix

New Facebook programming tool fixes bugs in code 'on the run' automatically
08 November 2018   910

Facebook described how Getafix works. The developers of the company created it to automate the process of fixing the code. Getafix offers fixes for bugs found by the Infer static analyzer, Sapfix and Sapienz, the application testing system.

The tool was created with the aim of shifting the routine duties of engineers to find and fix bugs by AI. In this case, the final decision on making changes is made by the person. The neural network uses the tools to consiser for the previous changes made by engineers, checks the new code and the context of the fragment. After these steps, it offers the option of a fix to the engineer.

Tools that automatically fix code are mostly designed for simple tasks, without context. Getafix, even in the case of similar bugs, can offer different solutions:

The company compared the changes made by man and AI, with the correction of about two hundred bugs. A quarter of the options proposed by the neural network coincided with human-written solutions.

Another experiment involved the correction of 2 thousand bugs calling the null pointer method. Getafix automatically fixed 53% of errors.

Facebook developed an AI-based tool for generating and deploying patches called Sapfix in mid-September 2018. The company introduced it at the Scale 2018 conference. Sapfix can work on its own or in combination with Sapienz - this is “smart” testing software from Facebook for finding errors in the code.

Neural Network to Create Landscapes from Sketches

Nvidia created GauGAN model that uses generative-competitive neural networks to process segmented images and create beautiful landscapes from peoples' sketches
20 March 2019   138

At the GTC 2019 conference, NVIDIA presented a demo version of the GauGAN neural network, which can turn sketchy drawings into photorealistic images.

The GauGAN model, named after the famous artist Paul Gauguin, uses generative-competitive neural networks to process segmented images. The generator creates an image and transfers it to the discriminator trained in real photographs. He in turn pixel-by-pixel tells the generator what to fix and where.

Simply put, the principle of the neural network is similar to the coloring of the coloring, but instead of children's drawings, it produces beautiful landscapes. Its creators emphasize that it does not just glue pieces of images, but generates unique ones, like a real artist.

Among other things, the neural network is able to imitate the styles of various artists and change the times of the day and year in the image. It also generates realistic reflections on water surfaces, such as ponds and rivers.

So far, GauGAN is configured to work with landscapes, but the neural network architecture allows us to train it to create urban images as well. The source text of the report in PDF is available here.

GauGAN can be useful to both architects and city planners, and landscape designers with game developers. An AI that understands what the real world looks like will simplify the implementation of their ideas and help you quickly change them. Soon the neural network will be available on the AI ​​Playground.