Abstract. To detect hard-to-find concurrency bugs, current tools systematically explore all possible interleavings of the transitions in a program. Unfortunately, concurrent programs have a large number of possible interleavings due to nondeterminism. Speeding up such tools requires pruning the state space explored. Partial-order reduction (POR) techniques can substantially prune the number of explored interleavings. These techniques require defining a dependency relation on transitions in the program, and exploit independency among certain transitions to prune the state space. We observe that actor systems, a prevalent class of programs where computation entities communicate by exchanging messages, exhibit a dependency relation among co-enabled transitions with an interesting property: transitivity. This paper introduces a novel dynamic POR technique, TransDPOR, that exploits the transitivity of the dependency relation in actor systems. Empirical results show that leveraging transitivity speeds up exploration by up to two orders of magnitude compared to existing POR techniques.

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Abstract. Actor programs consist of a number of concurrent objects called actors, which communicate by exchanging messages. Nondeterminism in actors results from the different possible orders in which available messages are processed. Systematic testing of actor programs explores various feasible message processing schedules. Dynamic partial-order reduction (DPOR) techniques speed up systematic testing by pruning parts of the exploration space. Based on the exploration of a schedule, a DPOR algorithm may find that it need not explore some other schedules. However, the potential pruning that can be achieved using DPOR is highly dependent on the order in which messages are considered for processing. This paper evaluates a number of heuristics for choosing the order in which messages are explored for actor programs, and summarizes their advantages and disadvantages.

We present a novel technique that speeds up state-space exploration (SSE) for evolving programs with dynamically allocated data. SSE is the essence of explicit-state model checking and an increasingly popular method for automating test generation. Traditional, non-incremental SSE takes one version of a program and systematically explores the states reachable during the program's executions to find property violations. Incremental SSE considers several versions that arise during program evolution: reusing the results of SSE for one version can speed up SSE for the next version, since state spaces of consecutive program versions can have significant similarities. We have implemented our technique in two model checkers: Java PathFinder and the J-Sim statespace explorer. The experimental results on 24 program evolutions and exploration changes show that for non-initial runs our technique speeds up SSE in 22 cases from 6.43% to 68.62% (with median of 42.29%) and slows down SSE in only two cases for -4.71% and -4.81%.

State-space exploration is the essence of model checking and an increasingly popular approach for automating test generation. A key issue in exploration of object-oriented programs is handling the program state, in particular the heap. Previous research has focused on standard program execution that operates on one state/heap. We present Delta Execution, a technique that simultaneously operates on several states/heaps. Delta execution exploits the fact that many execution paths in state-space exploration partially overlap and speeds up the exploration by sharing the common parts across the executions and separately executing only the "deltas" where the executions differ. The heart of Delta Execution is an efficient representation and manipulation of sets of states/heaps.We have implemented Delta Execution in two model checkers: JPF and BOX. JPF is a popular general-purpose model checker for Java programs, and BOX is a specialized model checker that we have developed for efficient exploration of sequential Java programs. We have evaluated Delta Execution for (bounded) exhaustive exploration of ten basic subject programs without errors. The experimental results show that on average Delta Execution improves the exploration time 10.97x (over an order of magnitude) in JPF and 2.07x in BOX, while taking on average 1.51x less memory in JPF and roughly the same amount of memory in BOX. We have also evaluated Delta Execution for one larger case study with errors, where the exploration time improved up to 1.43x. Additionally, the experimental results for abstract matching, a recently proposed non-exhaustive exploration in JPF, of four subject programs show that on average Delta Execution improves the exploration time 3.37x.