A survey and a taxonomy of approaches for testing parallel and distributed programs

Testing is a difficult and time-consuming part of the software development cycle. This is because an error may happen in an unexpected way at an unexpected spot. Testing and debugging parallel and distributed software are much more difficult than sequential software. This is due to the fact that errors are usually reproducible in sequential programs while they may not be reproducible in parallel and distributed programs. In addition, parallel and distributed programs introduce new types of errors and anomalies, race conditions and deadlocks, that do not exist in sequential software. I present a survey and a taxonomy of existing approaches for detecting race conditions and deadlocks in parallel and distributed programs. These approaches can be classified into two main classes: static analysis techniques, and dynamic analysis techniques. I have subdivided further static analysis techniques into three different subgroups: concurrency analysis methods; data-flow analysis methods; and formal proof methods. A brief discussion highlighting the main problems in most known approaches is given. The paper concludes with tables summarizing the comparison between the surveyed approaches.