João Lourenço

A set of methods defining an API (Application Programming Interface) are to be made thread safe; thus running any subset of these methods in parallel should not create races or deadlocks. Originally, the set of methods were not designed to be thread safe, so races and deadlocks are expected when running them in parallel. The number of possible interleavings when running methods from this API in parallel is huge, and this work focuses on the identification of the high level data races introduced by such interleavings. We propose an analysis that avoids the exhaustive exploration of all possible interleavings. For a concurrent program P, the closure of P, clos(P), is defined. Roughly speaking, we can say that the clos(P) is obtained by adding threads to P in such a way that high level data races resulting from running P in parallel to other programs are exposed statically. A set of methods representing the API is then modeled as a set of concurrent programs and their closure is analysed to identify high level data races. These high level data races are then inspected and removed to make the API thread safe. We illustrate the application of this methodology with a simple use case.

This special issue concerns a range of issues related to the development of concurrent programs. This is an important topic, because many systems are now either multi-threaded or distributed, and it is well known that concurrency makes testing, analysis and debugging significantly more complicated. Essentially, the alternative interleavings of events can lead to different behaviours, and so any analysis, debugging or testing technique must consider these interleavings. The interest in this topic is reflected in the larger than normal issue, which contains five papers. The papers fall into three groups: we start with a paper on debugging, then have two on static analysis techniques and finally have two on testing. All papers were reviewed in the normal way.

Transactional memory (TM) is an increasingly popular technique for synchronising threads in multi-threaded programs. To address both correctness and performance-related issues of TM programs, one needs to monitor and analyse their execution. However, monitoring concurrent programs (including TM programs) may have a non-negligible impact on their behaviour, which may hamper the objectives of the intended analysis. In this paper, we propose several approaches for monitoring TM programs and study their impact on the behaviour of the monitored programs. The considered approaches range from specialised lightweight monitoring to generic heavyweight monitoring. The implemented monitoring tools are publicly available to the scientific community, and the implementation techniques used for lightweight monitoring of TM programs may be used as an inspiration for developing other specialised lightweight monitors.