TECHNIQUES TO DETECT AND AVERT ADVANCED SOFTWARE CONCURRENCY BUGS BY

Multicore machines have become pervasive and, as a result, parallel programming has received renewed interest. Unfortunately, writing correct parallel programs is notoriously hard. One chal-lenging problem is how to ship correct programs. Dynamic analysis tools are very useful to find errors in parallel programs by automatically analyzing the runtime information. They often capture errors from well-tested programs. However, existing dynamic analysis tools suffer from two problems: high false positive rate and high overhead. High false positive rate means lots of errors reported by the dynamic analysis tool may be benign or non-existent. For example, lots of data races detected by a race detection tool could be relatively benign data races. Also, many dynamic software analyses cause orders-of-magnitude slowdowns, which users cannot tolerate at runtime. This dissertation contains three parts. The first two parts propose two different schemes to reduce the false positives and overhead of race detecting tools. These two schemes can detect and tolerate two different types of harmful races with low overhead: asymmetric data races and IF-condition data races