Interrupt-driven programs have been widely used in safety-critical areas such as aerospace and embedded systems. However, uncertain interleaving execution of interrupt service routines (ISRs) usually causes concurrency bugs. Specifically, when one or more ISRs attempt to preempt a sequence of instructions which are expected to be atomic, a kind of concurrency bugs namely atomicity violation may occur, and it is challenging to find this kind of bugs precisely and efficiently. In this paper, we propose a static approach for detecting atomicity violations in interrupt-driven programs. First, the program model is constructed with interruption points being selected to determine the possibly influenced ISRs. After that, reachability computation is conducted to build up a whole abstract reachability tree, and a delayed ISR-triggering strategy is employed to reduce the state space. Meanwhile, unserializable interleaving patterns are recognized to achieve the goal of atomicity violation detection. The approach has been implemented as a configurable tool namely CPA4AV. Extensive experiments show that CPA4AV is much more precise than the relative tools available with little extra time overhead. In addition, more complex situations can be dealt with CPA4AV.

Abstract is a tool for verifying reachability properties of concurrent C programs. It moderates the trace-space explosion problem, aggravated by thread alternation, through utilizing the PC-DPOR and C-Intp techniques. The PC-DPOR technique constructs a constrained dependency graph to refine dependencies between transitions. With this basis, the inherent imprecision of the dependence over-approximation can be overcome. Thereby, many redundant equivalent traces are prevented from being explored. On the other hand, the C-Intp technique performs conditional interpolation to confine the reachable regions of states, so that infeasible conditional branches which occur more frequently in concurrent verification tasks could be pruned automatically. We have implemented the above techniques on top of the open-source program analysis framework .

One of the most exciting applications of modern artificial intelligence is to automatically discover scientific laws from experimental data. This is not a trivial problem as it involves searching for a complex mathematical relationship over a large set of explanatory variables and operators that can be combined in an infinite number of ways. Inspired by the incredible success of deep learning in computer vision, we tackle this problem by adapting various successful network architectures into the symbolic law discovery pipeline. The novelty of our approach is in (1) encoding the input data as an image with super-resolution, (2) developing an appropriate deep network pipeline, and (3) predicting the importance of each mathematical operator from the relationship image. This allows us to prior the exponentially large search with the predicted importance of the symbolic operators, which can significantly accelerate the discovery process. We apply our model to a variety of plausible relationships---both simulated and from physics and mathematics domains---involving different dimensions and constituents. We show that our model is able to identify the underlying operators from data, achieving a high accuracy and AUC (91% and 0.96 on average resp.) for systems with as many as ten independent variables. Our method significantly outperforms the current state of the art in terms of data fitting (R^2), discovery rate (recovering the true relationship), and succinctness (output formula complexity). The discovered equations can be seen as first drafts of scientific laws that can be helpful to the scientists for (1) hypothesis building, and (2) understanding the complex underlying structure of the studied phenomena. Our approach holds a real promise to help speed up the rate of scientific discovery.