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Compiler bugs can be disastrous since they could affect all the software systems built on the buggy compilers. Meanwhile, diagnosing compiler bugs is extremely challenging since usually limited debugging information is available and a large number of compiler files can be suspicious. More specifically, when compiling a given bug-triggering test program, hundreds of compiler files are usually involved, and can all be treated as suspicious buggy files. To facilitate compiler debugging, in this paper we propose the first reinforcement compiler bug isolation approach via structural mutation, called RecBi. For a given bug-triggering test program, RecBi first augments traditional local mutation operators with structural ones to transform it into a set of passing test programs. Since not all the passing test programs can help isolate compiler bugs effectively, RecBi further leverages reinforcement learning to intelligently guide the process of passing test program generation. Then, RecBi ranks all the suspicious files by analyzing the compiler execution traces of the generated passing test programs and the given failing test program following the practice of compiler bug isolation. The experimental results on 120 real bugs from two most popular C open-source compilers, i.e., GCC and LLVM, show that RecBi is able to isolate aboutmore »23%/58%/78% bugs within Top-1/Top-5/Top-10 compiler files, and significantly outperforms the state-of-the-art compiler bug isolation approach by improving 92.86%/55.56%/25.68% isolation effectiveness in terms of Top-1/Top-5/Top-10 results.« less

Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.

Deep neural network (DNN) has become increasingly popular and DNN testing is very critical to guarantee the correctness of DNN, i.e., the accuracy of DNN in this work. However, DNN testing suffers from a serious efficiency problem, i.e., it is costly to label each test input to know the DNN accuracy for the testing set, since labeling each test input involves multiple persons (even with domain-specific knowledge) in a manual way and the testing set is large-scale. To relieve this problem, we propose a novel and practical approach, called PACE (which is short for P ractical AC curacy E stimation), which selects a small set of test inputs that can precisely estimate the accuracy of the whole testing set. In this way, the labeling costs can be largely reduced by just labeling this small set of selected test inputs. Besides achieving a precise accuracy estimation, to make PACE more practical it is also required that it is interpretable, deterministic, and as efficient as possible. Therefore, PACE first incorporates clustering to interpretably divide test inputs with different testing capabilities (i.e., testing different functionalities of a DNN model) into different groups. Then, PACE utilizes the MMD-critic algorithm, a state-of-the-art example-based explanation algorithm,more »to select prototypes (i.e., the most representative test inputs) from each group, according to the group sizes, which can reduce the impact of noise due to clustering. Meanwhile, PACE also borrows the idea of adaptive random testing to select test inputs from the minority space (i.e., the test inputs that are not clustered into any group) to achieve great diversity under the required number of test inputs. The two parallel selection processes (i.e., selection from both groups and the minority space) compose the final small set of selected test inputs. We conducted an extensive study to evaluate the performance of PACE based on a comprehensive benchmark (i.e., 24 pairs of DNN models and testing sets) by considering different types of models (i.e., classification and regression models, high-accuracy and low-accuracy models, and CNN and RNN models) and different types of test inputs (i.e., original, mutated, and automatically generated test inputs). The results demonstrate that PACE is able to precisely estimate the accuracy of the whole testing set with only 1.181%∼2.302% deviations, on average, significantly outperforming the state-of-the-art approaches.« less