Data races are the most common concurrency bugs and considerable efforts are put in ensuring data-race-free (DRF) programs. The most popular approach is via dynamic analyses, which soundly report DRF violations by analyzing program executions. Recently, there has been a prevalent shift to predictive analysis techniques. Such techniques attempt to predict DRF violations even in unobserved program executions, while making sure that the analysis is sound (does not raise false positives).

This tutorial will present the foundations of race prediction in a systematic manner, and summarize latest advances in race prediction in a concise and unifying way. State-of-the-art predictive techniques will be explained out of first principles, followed by a comparison between soundness, completeness and complexity guarantees provided in each case. In addition, we will highlight the use of specific data structures that result in algorithmic efficiency in these techniques. We will also touch on various notions of optimality and their suitability in online/offline prediction. On the theoretical side, we will highlight some recent hard computational barriers inherent in race prediction, as well as ways to alleviate them in specific settings. We will also touch upon other common concurrency bugs, such as deadlocks and atomicity violations, and highlight cases when techniques are transferable between them. The tutorial will include a hands-on demonstration of two relevant tools, namely RAPID and M2. Finally, we will end with some key open questions with the aim to inspire future research.