Machine Learning Used For JavaScript Code De-obfuscation 31
New submitter velco writes: "ETH Zurich Software Reliability Lab announced JSNice, a statistical de-obfuscation and de-minification tool for JavaScript. The interesting thing about JSNice is that it combines program analysis with machine learning techniques to build a database of name and type regularities from large amounts of available open source code on GitHub. Then, given new JavaScript code, JSNice tries to infer the most likely names and types for that code by basing its decision on the learned regularities in the training phase."
Hahahaha! (Score:4, Funny)
The development of tools like these started out of necessity for figuring out old COBOL code.
Finally consistent naming (Score:5, Funny)
Now we just run every JavaScript program through an obfuscator then JSNice and we have consistent naming.
Re:Hahahaha! (Score:2, Funny)
That would be
"DIVIDE REC-WORKER-TOTAL-ANNUAL-SALARY BY WS-HOURS-IN-FISCAL-YEAR
GIVING WS-HOURLY-RATE REMAINDER WS-ANNUAL-BONUS."
or something similar.