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 exploration-exploitation 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.

In practice, a program may contain multiple bugs. The simultaneous presence of these bugs may deteriorate the effectiveness of existing fault-localization techniques to locate program bugs. While it is acceptable to use all failed and successful tests to identify suspicious code for programs with exactly one bug, it is not appropriate to use the same approach for programs with multiple bugs because the due-to relationship between failed tests and underlying bugs cannot be easily identified. One solution is to generate fault-focused clusters by grouping failed tests caused by the same bug into the same clusters. We propose MSeer- an advanced fault localization technique for locating multiple bugs in parallel. Our major contributions include the use of (1) a revised Kendall tau distance to measure the distance between two failed tests, (2) an innovative approach to simultaneously estimate the number of clusters and assign initial medoids to these clusters, and (3) an improved K-medoids clustering algorithm to better identify the due-to relationship between failed tests and their corresponding bugs. Case studies on 840 multiple-bug versions of seven programs suggest that MSeer performs better in terms of effectiveness and efficiency than two other techniques for locating multiple bugs in parallel.

An ongoing, annual survey of publications in systems and software engineering identifies the top 15 scholars and institutions in the field over a 5-year period. Each ranking is based on the weighted scores of the number of papers published in TSE, TOSEM, JSS, SPE, EMSE, IST, and Software of the corresponding period. This report summarizes the results for 2003–2007 and 2004–2008. The top-ranked institution is Korea Advanced Institute of Science and Technology, Korea for 2003–2007, and Simula Research Laboratory, Norway for 2004–2008, while Magne Jørgensen is the top-ranked scholar for both periods.