Transactional Memory (TM) is an approach to concurrency control in general pur- pose programming languages that inherits the concept of transaction from the database setting. Unlike other language constructs such as locks, TM has an optimistic approach to concurrency control by allowing more than one thread to access simultaneously the same critical section. A transaction always executes as if it is alone in the system, and in the end its effects are undone (rolled back) if it conflicts with another concurrent transac- tions. In spite of the potential for increasing scalability and performance, TM is a recent and developing programming model and still has a very limited impact in real-world applications.
Designing and developing concurrent software is difficult and error prone. Concur- rent programs exhibit concurrency anomalies that originate faults and failures. Despite some claims that TM programs are less error prone, they still exhibit concurrency anoma- lies such as high-level dataraces, i.e., wrong delimitations of transactions’ scope, and stale-value errors, that occur when the value of a shared variable jumps from an atomic block to another.
Programs with this kind of anomalies can exhibit unpredictable and wrong behaviour, not fulfilling the goals for which they were conceived.
This work aims the detection of anomalies through static analysis of transactional Java ByteCode programs that execute in strong atomicity. A extensible and flexible framework is proposed, which can be extended with plugins that detect specific types of anomalies. With this framework we expect to prove that high-level dataraces and stale-value errors can be detected with reasonable precision through static analysis.

Keywords: Atomicity Violation, High-Level Datarace, Static Analysis, Concurrency, Software Transactional Memory

In this paper we present MoTh, a tool that uses static analysis to enable the automatic verification of concurrency anomalies in Transactional Memory Java programs. Currently MoTh detects high-level dataraces and stale-value errors, but it is extendable by plugging-in sensors, each sensor implementing an anomaly detecting algorithm. We validate and benchmark MoTh by applying it to a set of well known concurrent buggy programs and by close comparison of the results with other similar tools. The results achieved so far are very promising, yielding good accuracy while triggering only a very limited number of false warnings.

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