Programs containing concurrency anomalies will most probably 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) programs should also be diagnosed and fixed. In this paper we propose a framework to deal with two different categories of concurrency anomalies in TM. First, we will address low-level TM anomalies, also called dataraces, which arise from executing programs in weak isolation. Secondly, 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.