Data races vs. data race bugs: telling the difference with portend

Even though most data races are harmless, the harmful ones are at the heart of some of the worst concurrency bugs. Alas, spotting just the harmful data races in programs is like finding a needle in a haystack: 76%-90% of the true data races reported by state-of-theart race detectors turn out to be harmless [45]. We present Portend, a tool that not only detects races but also automatically classifies them based on their potential consequences: Could they lead to crashes or hangs? Could their effects be visible outside the program? Are they harmless? Our proposed technique achieves high accuracy by efficiently analyzing multiple paths and multiple thread schedules in combination, and by performing symbolic comparison between program outputs. We ran Portend on 7 real-world applications: it detected 93 true data races and correctly classified 92 of them, with no human effort. 6 of them are harmful races. Portend’s classification accuracy is up to 89% higher than that of existing to...

• Type: Journal
• Year: 2012
• Where: ASPLOS
• Authors: Baris Kasikci, Cristian Zamfir, George Candea
• Keywords: ASPLOS 2012, Multiple Thread, Needle In A Haystack, Program Outputs, Programming Languages