Facebook to Unveil Getafix

New Facebook programming tool fixes bugs in code 'on the run' automatically
08 November 2018   2182

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.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   464

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.