We consider the problem of detecting data races in program traces that have been compressed using straight line programs (SLP), which are special context-free grammars that generate exactly one string, namely the trace that they represent. We consider two classical approaches to race detection --- using the happens-before relation and the lockset discipline. We present algorithms for both these methods that run in time that is linear in the size of the compressed, SLP representation. Typical program executions almost always exhibit patterns that lead to significant compression. Thus, our algorithms are expected to result in large speedups when compared with analyzing the uncompressed trace. Our experimental evaluation of these new algorithms on standard benchmarks confirms this observation.

Dynamic race detection is the problem of determining if an observed program execution reveals the presence of a data race in a program. The classical approach to solving this problem is to detect if there is a pair of conflicting memory accesses that are unordered by Lamport’s happens-before (HB) relation. HB based race detection is known to not report false positives, i.e., it is sound. However, the soundness guarantee of HB only promises that the first pair of unordered, conflicting events is a schedulable data race. That is, there can be pairs of HB-unordered conflicting data accesses that are not schedulable races because there is no reordering of the events of the execution, where the events in race can be executed immediately after each other. We introduce a new partial order, called schedulable happens-before (SHB) that exactly characterizes the pairs of schedulable data races — every pair of conflicting data accesses that are identified by SHB can be scheduled, and every HB-race that can be scheduled is identified by SHB. Thus, the SHB partial order is truly sound. We present a linear time, vector clock algorithm to detect schedulable races using SHB. Our experiments demonstrate the value of our algorithm for dynamic race detection — SHB incurs only little performance overhead and can scale to executions from real-world software applications without compromising soundness.

Given a Markov Decision Process (MDP) M, an LTL formula \varphi, and a threshold \theta \in [0,1], the verification question is to determine if there is a scheduler with respect to which the executions of M satisfying \varphi have probability greater than (or greater than or equal to) \theta. When \theta = 0, we call it the qualitative verification problem, and when \theta \in (0,1], we call it the quantitative verification problem. In this paper we study the precise complexity of these problems when the specification is constrained to be in different fragments of LTL.

Writing reliable concurrent software remains a huge challenge for today's programmers. Programmers rarely reason about their code by explicitly considering different possible inter-leavings of its execution. We consider the problem of detecting data races from individual executions in a sound manner. The classical approach to solving this problem has been to use Lamport's happens-before (HB) relation. Until now HB remains the only approach that runs in linear time. Previous efforts in improving over HB such as causally-precedes (CP) and maximal causal models fall short due to the fact that they are not implementable efficiently and hence have to compromise on their race detecting ability by limiting their techniques to bounded sized fragments of the execution. We present a new relation weak-causally-precedes (WCP) that is provably better than CP in terms of being able to detect more races, while still remaining sound. Moreover, it admits a linear time algorithm which works on the entire execution without having to fragment it.

In computer-aided education, the goal of automatic feedback is to provide a meaningful explanation of students' mistakes. We focus on providing feedback for constructing a deterministic finite automaton that accepts strings that match a described pattern. Natural choices for feedback are binary feedback (correct/wrong) and a counterexample of a string that is processed incorrectly. Such feedback is easy to compute but might not provide the student enough help. Our first contribution is a novel way to automatically compute alternative conceptual hints. Our second contribution is a rigorous evaluation of feedback with 377 students. We find that providing either counterexamples or hints is judged as helpful, increases student perseverance, and can improve problem completion time. However, both strategies have particular strengths and weaknesses. Since our feedback is completely automatic, it can be deployed at scale and integrated into existing massive open online courses.

The reachability problem for timed automata asks if there exists a path from an initial state to a target state. The standard solution to this problem involves computing the zone graph of the automaton, which in principle could be infinite. In order to make the graph finite, zones are approximated using an extrapolation operator. For reasons of efficiency in current algorithms extrapolation of a zone is always a zone; and in particular it is convex. In this paper, we propose to solve the reachability problem without such extrapolation operators. To ensure termination, we provide an efficient algorithm to check if a zone is included in the so called region closure of another. Although theoretically better, closure cannot be used in the standard algorithm since a closure of a zone may not be convex. An additional benefit of the proposed approach is that it permits to calculate approximating parameters on-the-fly during exploration of the zone graph, as opposed to the current methods which do it by a static analysis of the automaton prior to the exploration. This allows for further improvements in the algorithm. Promising experimental results are presented.