Guided Testing of Concurrent Programs Using Value Schedules

Testing concurrent programs remains a difficult task due to the non-deterministic nature of concurrent execution. Many approaches have been proposed to tackle the complexity of uncovering potential concurrency bugs. Static analysis tackles the problem by analyzing a concurrent program looking for situations/patterns that might lead to possible errors during execution. In general, static analysis cannot precisely locate all possible concurrent errors. Dynamic testing examines and controls a program during its execution also looking for situations/patterns that might lead to possible errors during execution. In general, dynamic testing needs to examine all possible execution paths to detect all errors, which is intractable. Motivated by these observation, a new testing technique is developed that uses a collaboration between static analysis and dynamic testing to find the first potential error but using less time and space. In the new collaboration scheme, static analysis and dynamic testing interact iteratively throughout the testing process. Static analysis provides coarse-grained flow-information to guide the dynamic testing through the relevant search space, while dynamic testing collects concrete runtime-information during the guided exploration. The concrete runtime-information provides feedback to the static analysis to refine its analysis, which is then feed forward to provide more precise guidance of the dynamic testing. The new collaborative technique is able to uncover the first concurrency-related bug in a program faster using less storage than the state-of-the-art dynamic testing-tool Java PathFinder. The implementation of the collaborative technique consists of a static-analysis module based on Soot and a dynamic-analysis module based on Java PathFinder

Sequentializing Parameterized Programs

We exhibit assertion-preserving (reachability preserving) transformations from parameterized concurrent shared-memory programs, under a k-round scheduling of processes, to sequential programs. The salient feature of the sequential program is that it tracks the local variables of only one thread at any point, and uses only O(k) copies of shared variables (it does not use extra counters, not even one counter to keep track of the number of threads). Sequentialization is achieved using the concept of a linear interface that captures the effect an unbounded block of processes have on the shared state in a k-round schedule. Our transformation utilizes linear interfaces to sequentialize the program, and to ensure the sequential program explores only reachable states and preserves local invariants.Comment: In Proceedings FIT 2012, arXiv:1207.348

A Study of Concurrency Bugs and Advanced Development Support for Actor-based Programs

The actor model is an attractive foundation for developing concurrent applications because actors are isolated concurrent entities that communicate through asynchronous messages and do not share state. Thereby, they avoid concurrency bugs such as data races, but are not immune to concurrency bugs in general. This study taxonomizes concurrency bugs in actor-based programs reported in literature. Furthermore, it analyzes the bugs to identify the patterns causing them as well as their observable behavior. Based on this taxonomy, we further analyze the literature and find that current approaches to static analysis and testing focus on communication deadlocks and message protocol violations. However, they do not provide solutions to identify livelocks and behavioral deadlocks. The insights obtained in this study can be used to improve debugging support for actor-based programs with new debugging techniques to identify the root cause of complex concurrency bugs.Comment: - Submitted for review - Removed section 6 "Research Roadmap for Debuggers", its content was summarized in the Future Work section - Added references for section 1, section 3, section 4.3 and section 5.1 - Updated citation

Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)

Dynamic data race prediction aims to identify races based on a single program run represented by a trace. The challenge is to remain efficient while being as sound and as complete as possible. Efficient means a linear run-time as otherwise the method unlikely scales for real-world programs. We introduce an efficient, near complete and often sound dynamic data race prediction method that combines the lockset method with several improvements made in the area of happens-before methods. By near complete we mean that the method is complete in theory but for efficiency reasons the implementation applies some optimizations that may result in incompleteness. The method can be shown to be sound for two threads but is unsound in general. We provide extensive experimental data that shows that our method works well in practice.Comment: typos, appendi

Analyzing Conflict Freedom For Multi-threaded Programs With Time Annotations

Avoiding access conflicts is a major challenge in the design of multi-threaded programs. In the context of real-time systems, the absence of conflicts can be guaranteed by ensuring that no two potentially conflicting accesses are ever scheduled concurrently.In this paper, we analyze programs that carry time annotations specifying the time for executing each statement. We propose a technique for verifying that a multi-threaded program with time annotations is free of access conflicts. In particular, we generate constraints that reflect the possible schedules for executing the program and the required properties. We then invoke an SMT solver in order to verify that no execution gives rise to concurrent conflicting accesses. Otherwise, we obtain a trace that exhibits the access conflict.Comment: http://journal.ub.tu-berlin.de/eceasst/article/view/97