A Static Approach for Detecting Concurrency Anomalies in Transactional Memory

Abstract. Programs containing concurrency anomalies will most prob-ably exhibit harmful erroneous and unpredictable behaviors. To ensure program correctness, the sources of those anomalies must be located and corrected. Concurrency anomalies in Transactional Memory (TM) pro-grams should also be diagnosed and fixed. In this paper we propose a framework to deal with two different categories of concurrency anoma-lies in TM. First, we will address low-level TM anomalies, also called dataraces, which arise from executing programs in weak isolation. Sec-ondly, we will address high-level TM anomalies, also called high-level dataraces, bringing the programmer's attention to pairs of transactions that the programmer has misspecified, and should have been combined into a single transaction. Our framework was validated against a set of programs with well known anomalies and demonstrated high accuracy and effectiveness, thus contributing for improving the correctness of TM programs