Safety-critical systems such as self-driving cars (SDCs) must be rigorously tested. Especially electronic control units (ECUs) of SDCs should be tested with realistic input data. In this context, a communication protocol called Controller Area Network (CAN) is typically used to transfer sensor data to the SDC control units. A challenge for SDC maintainers and testers is the need to manually define the CAN inputs that realistically represent the state of the SDC in the real world. To address this challenge, we developed TEASER; a tool that generates realistic CAN signals for SDCs obtained from sensors from state-of-the-art car simulators. We evaluated TEASER based on its integration capability into a DevOps pipeline of aicas GmbH, a company in the automotive sector. Concretely, we integrated TEASER in a Continous Integration (CI) pipeline configured with Jenkins. The pipeline executes the test cases in simulation environments and sends the sensor data over the CAN bus to a physical CAN device, the test subject. Our evaluation shows the ability of TEASER to generate and execute CI test cases that expose simulation-based faults (using regression strategies); the tool produces CAN inputs that realistically represent the state of the SDC in the real world. This result is critically important for increasing the automation and effectiveness of simulation-based CAN bus regression testing for SDCs.

We present a novel approach to bounded program verification that exploits recent advances of SMT solvers in modular checking of object-oriented code against its full specification. Bounded program verification techniques exhaustively check the specifications of a bounded program with respect to a bounded domain. To our knowledge, however, those techniques that target data-structure-rich programs reduce the problem to propositional logic directly, and use a SAT solver as the backend engine. Scalability, therefore, becomes a major issue due to bit blasting problems. In this paper, we present a novel approach that translates bounded Java programs and their JML specifications to quantified bit-vector formulas (QBVF) with arrays, and solves them using an SMT solver. QBVF allows logical constraints that are structurally closer to the original program and specification, and can be significantly simplified via high-level reasonings before being flattened in a basic logic. We also present a case study on a large-scale implementation of Dijkstra's shortest path algorithm. The results indicate that our approach provides significant speedups over a SAT-based approach.

Linfen area is dominated by a series of strike-slip active faults and is presently characterized by low or medium seismicity. The Multi-temporal Interferometry SAR (MT-InSAR) methodology provides high resolution assessment of surface deformations over long periods of observation. Stanford Method for Persistent Scatterers (StaMPS) includes a Permanent Scatterer InSAR (PS-InSAR) method and a Small Baseline (SB) method, which are the two categories of MT-InSAR. Over 100,000 PS velocity along the satellite Line Of Sight (LOS) was calculated using ENVISAT images acquired as well as the time series of LOS displacement based on SB method of StaMPS, which mainly show a successive dynamic pattern, i.e., mountains or sub-uplift zones keep rising, and basins or depressions remain subsiding. Moreover, uplift with large scale emerges in the region where Hancheng Fault runs crossing Luoyun Piedmont Fault, which is perhaps caused by the being obstructed of its right-lateral slip. As a result, crustal stress field is accumulating and seismic hazard becomes increasing.