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Unmanned aerial vehicles (UAVs) are playing an increasingly important role in people's daily lives due to their low cost of operation, low requirements for ground support, high maneuverability, high environmental adaptability, and high safety. Yet UAV path planning under various safety risks, such as crash and collision, is not an easy task, due to the complicated and dynamic nature of path environments. Therefore, developing an efficient and flexible algorithm for UAV path planning has become inevitable. Aimed at quality-oriented UAV path planning, this paper is designed to analyze UAV path planning from two aspects: global static planning and local dynamic hierarchical planning. Through a theoretical and mathematical approach, a three-dimensional UAV path planning model was established. Based on the A* algorithm, the search strategy, the step size, and the cost function were improved, and the OPEN set was simplified, thereby shortening the planning time and greatly improving the execution efficiency of the algorithm. Moreover, a dynamic exploration factor was added to the exploration mechanism of Q-learning to solve the explorationexploitation dilemma of Q-learning to adapt to the local dynamic path adjustment for UAVs. The global-local hybrid UAV path planning algorithm was formed by combining the two. The simulation results indicate that the proposed planning model and algorithm can efficiently solve the problem of UAV path planning, improve the path quality, and can be a significant reference for solving other problems related to path planning, such as the reliability, security, and safety of UAV, when embedded into the heuristic function of the proposed algorithm.

The challenge of automatically repairing bugs in programs to reduce debugging expenses and increase program quality is known as automated program repair. To overcome this issue, test-suite-based repair techniques use a specified test suite as an oracle and alter the input faulty program to pass the full test suite. GenProg is a well-known example of this kind of repair, in which genetic programming is used to reorder the statements already present in the faulty program. However, recent practical experiments suggest that GenProg's performance, notably for Java, is not sufficient. Improved program dependability necessitates the use of automatic program repair techniques. Template-based program repair techniques have recently been combined with search-based techniques to solve program issues automatically. Although intriguing, it has two fundamental drawbacks: Its search space often lacks the correct solution, and the technique disregards program expertise, such as precise code language. Compared with the template-based program repair approach, existing neural-machine-translation-based approaches are not limited by these constraints due to their ability to learn and generate new solutions. We propose an approach that combines a search-based automatic program repair technique with a neural-machine-translation-based approach. More specifically, we use both redundancy assumption and sequence-to-sequence learning of correct patches as the source for potential fix statements that feed into a multiobjective evolutionary search algorithm to find test-suiteadequate patches. In this work, a novel framework called ARJANMT is introduced for automatically repairing Java programs. Two sets of controlled experiments are conducted on 410 bugs from two benchmarks to investigate the repairability and correctness of our proposed framework. A comparison between state-of-the-art automatic program repair frameworks is made. The experimental results indicate that combining those two types of repair techniques (search-based and neural-machine-translation-based) produces better results or fixes bugs that they previously were unable to fix individually.

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